The document proposes an image processing methodology to automatically grade disease on pomegranate leaves and identify bacterial blight disease. It involves image acquisition, pre-processing, color segmentation using k-means clustering to extract diseased spots, and calculating disease and total leaf areas to determine the percent infection and corresponding disease grade. The system can identify bacterial blight by checking leaves for yellow margins around diseased areas and fruits for cracks passing through black spots. The results of automatic grading are more accurate and less time-consuming than manual grading.
This document provides information on major diseases that affect chili peppers, including damping off caused by Pythium spp., anthracnose caused by Colletotrichum capsici, and bacterial leaf spot caused by Xanthomonas campestris pv. Vesicatoria. It discusses symptoms, causal organisms, and disease cycles. It recommends an integrated pest management approach including crop rotation, certified seed, soil solarization, and fungicide or hot water seed treatment to manage diseases.
Soya bean crop diseases A Lecture by Mr Allah Dad KhanMr.Allah Dad Khan
This document summarizes 9 common soybean crop diseases:
1. Phytophthora seed and seedling blight, caused by the fungus Phytophthora sojae, which survives in soil for years. It infects seeds and seedlings, causing damping off. Management includes resistant varieties, fungicide seed treatments, and improved drainage.
2. Pythium seedling and root rot, caused by several Pythium species. It infects seeds and seedlings, causing soft rot. Management focuses on soil drainage, seed treatments, and planting in warmer soils.
3. Rhizoctonia root rot, caused by the fungus Rhizoctonia solani. It causes root and stem
This document summarizes several common diseases that affect chilli plants: damping-off caused by Pythium aphanidermatum, powdery mildew caused by Leveillula taurica, fruit rot and die-back caused by Colletotrichum capsici, leaf spot caused by Cercospora capsici, and bacterial spot caused by Xanthomonas Campestris Pv. vesicatoria. It describes the symptoms, etiology, mode of spread and survival, favorable conditions, and management recommendations for each disease.
This document summarizes 15 important diseases that affect rice, including their causal organisms, symptoms, modes of spread, survival methods, and management strategies. The major fungal diseases discussed are blast, brown spot, sheath blight, sheath rot, and stem rot. The major bacterial diseases are bacterial leaf blight and bacterial leaf streak. Viral diseases covered include tungro, grassy stunt, rice dwarf, and yellow dwarf. Other diseases summarized are false smut, udbatta disease, grain discoloration, and rice khaira deficiency. For each disease, the summary provides key details about identification and control.
Phomopsis blight is caused by the fungus Phomopsis vexans, which was first discovered infecting brinjal in 1914 in Southern Asia. It is a major pathogen of brinjal crops in India, causing up to 50% yield loss. The fungus produces small circular leaf spots and pale brown sunken spots on fruits. It survives in infected plant debris in soil and spreads via rain splashes, implements, insects and seeds. Hot and humid conditions favor its growth. Management strategies include crop rotation, burning debris, seed treatment, resistant varieties, and fungicide application.
- Grey mildew, caused by the fungus Mycosphaerella areola, is an important cotton disease that can cause major yield losses in India.
- The disease spreads via airborne spores during periods of low temperature and high humidity from October to January.
- Management of grey mildew involves cultural controls like deep plowing of fields to destroy crop residues between seasons as well as fungicide applications during periods of disease risk.
This document provides information on major diseases that affect chili peppers, including damping off caused by Pythium spp., anthracnose caused by Colletotrichum capsici, and bacterial leaf spot caused by Xanthomonas campestris pv. Vesicatoria. It discusses symptoms, causal organisms, and disease cycles. It recommends an integrated pest management approach including crop rotation, certified seed, soil solarization, and fungicide or hot water seed treatment to manage diseases.
Soya bean crop diseases A Lecture by Mr Allah Dad KhanMr.Allah Dad Khan
This document summarizes 9 common soybean crop diseases:
1. Phytophthora seed and seedling blight, caused by the fungus Phytophthora sojae, which survives in soil for years. It infects seeds and seedlings, causing damping off. Management includes resistant varieties, fungicide seed treatments, and improved drainage.
2. Pythium seedling and root rot, caused by several Pythium species. It infects seeds and seedlings, causing soft rot. Management focuses on soil drainage, seed treatments, and planting in warmer soils.
3. Rhizoctonia root rot, caused by the fungus Rhizoctonia solani. It causes root and stem
This document summarizes several common diseases that affect chilli plants: damping-off caused by Pythium aphanidermatum, powdery mildew caused by Leveillula taurica, fruit rot and die-back caused by Colletotrichum capsici, leaf spot caused by Cercospora capsici, and bacterial spot caused by Xanthomonas Campestris Pv. vesicatoria. It describes the symptoms, etiology, mode of spread and survival, favorable conditions, and management recommendations for each disease.
This document summarizes 15 important diseases that affect rice, including their causal organisms, symptoms, modes of spread, survival methods, and management strategies. The major fungal diseases discussed are blast, brown spot, sheath blight, sheath rot, and stem rot. The major bacterial diseases are bacterial leaf blight and bacterial leaf streak. Viral diseases covered include tungro, grassy stunt, rice dwarf, and yellow dwarf. Other diseases summarized are false smut, udbatta disease, grain discoloration, and rice khaira deficiency. For each disease, the summary provides key details about identification and control.
Phomopsis blight is caused by the fungus Phomopsis vexans, which was first discovered infecting brinjal in 1914 in Southern Asia. It is a major pathogen of brinjal crops in India, causing up to 50% yield loss. The fungus produces small circular leaf spots and pale brown sunken spots on fruits. It survives in infected plant debris in soil and spreads via rain splashes, implements, insects and seeds. Hot and humid conditions favor its growth. Management strategies include crop rotation, burning debris, seed treatment, resistant varieties, and fungicide application.
- Grey mildew, caused by the fungus Mycosphaerella areola, is an important cotton disease that can cause major yield losses in India.
- The disease spreads via airborne spores during periods of low temperature and high humidity from October to January.
- Management of grey mildew involves cultural controls like deep plowing of fields to destroy crop residues between seasons as well as fungicide applications during periods of disease risk.
The document describes post flowering stalk rot in maize, which is caused by the pathogens Cephalosporium acremonium and Cephalosporium maydis. The pathogens survive in soil, plant debris, and seed. Primary infection occurs through seed, while secondary infection spreads through wind. High temperatures and drought stress favor disease development. Management strategies include crop sanitation, crop rotation, avoiding water stress at flowering, seed treatment, and growing resistant varieties.
Prayers and sacrifices to gods for control of plant diseases
The mid-1600s, a species or variety was reported to be more resistant to a disease than another related species or variety.
Selection of resistant plants as a control of plant diseases.
This is likely to have occurred not only because seeds from resistant and therefore healthier plants looked bigger and better than those from infected susceptible plants, but also because in severe disease out breaks, resistant plants were the only ones surviving and, therefore, their seeds were the only ones available for planting.
Integrated disease management Maize diseases hema latha
This document discusses several diseases that affect maize crops. It begins by introducing maize as a major cereal crop in India and its economic importance. It then describes the major and sporadic diseases that affect maize, when they typically occur, and their potential yield losses. Several diseases are explained in more detail, including their symptoms, disease cycle, distribution, and management strategies. These include turcicum leaf blight, sorghum downy mildew, crazy top of corn, brown stripe downy mildew, and others. Management involves practices like using resistant varieties, crop rotation, removing debris, and fungicide application.
This document summarizes several diseases that affect apples:
1. Apple scab, caused by the fungus Venturia inaequalis, causes black spots on leaves and fruits. Spores are spread by wind and rain. Management includes clean cultivation, resistant varieties, and fungicide sprays.
2. Powdery mildew, caused by Podosphaera leucotricha, produces white or gray powdery patches on leaves, twigs, and fruits. Spores are wind-borne. Management includes sanitation, pre-bloom lime sulfur sprays, and resistant varieties.
3. Fire blight, caused by bacterium Erwinia amylovora, affects blossoms, shoots, branches
Diseases of Onion and garlic and their managementVAKALIYA MUSTUFA
This document provides information on diseases that affect onion and garlic crops and their management. It discusses several fungal diseases including downy mildew, purple blotch, stemphylium blight, basal rot/bulb rot, and rust. For each disease, it describes the symptoms, disease cycle, favorable conditions for development, and recommendations for management through cultural practices and fungicide applications. The overall document aims to review the major diseases of onion and garlic and provide strategies to control them.
The document describes two diseases that affect finger millet: blast and leaf spot. Blast is caused by Pyricularia oryzae and symptoms include brown spots on leaves that enlarge and cause foliage drying. It also causes neck blast where the neck turns black. Leaf spot is caused by Helminthosporium nodulosum and symptoms include small brown oval lesions on young leaves that coalesce into large patches and cause blighting. Both diseases can infect plants at any growth stage and cause yield losses. Management includes seed treatment and foliar fungicide applications.
This document discusses three important diseases of mango: powdery mildew, anthracnose, and mango malformation. It provides details on the symptoms, causal fungi, and favorable conditions for each disease. It also outlines management strategies for each disease, including cultural practices like pruning and spacing, resistant varieties, and fungicide application timings and active ingredients. Key information includes that powdery mildew can cause up to 80% crop loss, anthracnose impacts both pre-and post-harvest fruit, and malformation distorts flowers and shoots.
The document summarizes several diseases that affect marigold plants and their control methods. It describes diseases such as damping off caused by Rhizoctonia solani, leaf spots and blight caused by Alternaria, Cercospora and Septoria species, inflorescence blight caused by Alternaria zinnae, flower bud rot caused by Alternaria dianthi, and powdery mildew caused by Oidium sp. and Leveillula taurica. It provides details on symptoms, causal organisms, and recommendations for control which include soil drenching, fungicide spraying, and dusting with sulfur powder.
This document discusses the anthracnose disease that affects mango plants. It is caused by the fungus Colletotrichum gloeosporioides. The disease causes lesions on leaves, stems, flowers and fruits of mango plants. It thrives under warm and humid conditions between 24-32°C. The fungus overwinters on infected plant debris. Spores are spread by rain splash and irrigation water. Management involves spraying carbendazim during flowering and copper or mancozeb fungicides on leaves at 15 day intervals to control the disease.
This document summarizes bacterial wilt of brinjal, caused by the pathogen Ralstonia solanacearum. It affects eggplant as well as other crops like tomato and potato. Symptoms include sudden wilting and yellowing of leaves. The bacteria enter through wounds in roots and spread through the xylem, blocking water transport. Warm, moist conditions favor disease development. Management strategies include removing infected plants, crop rotation, and using resistant varieties.
This document discusses pomegranate production, post-harvest management techniques, and value addition opportunities for pomegranates. It provides an overview of pomegranate production levels in India and discusses the various health benefits of pomegranates. It then outlines several unit operations for post-harvest handling of fruits and vegetables including harvesting, cooling, storage, and transportation. Various techniques for pomegranate processing are described including aril extraction, minimal processing, and development of products like anardana powder, jelly, and tablets. Storage and cooling technologies like evaporative cool chambers and a two-stage evaporative cooler are also summarized. Finally, the document discusses entrepreneurship and training programs offered by the Central Institute of Post
This document discusses five common diseases that affect safflower: Alternaria blight, Sclerotinia head rot, root rot, gray mold, and rust. Alternaria blight causes dark lesions on stems and leaves and can rot seeds. Sclerotinia head rot causes heads to disintegrate, leaving sclerotia. Root rot causes wilting and bleaching as roots and stems rot. Gray mold causes soft rotting at wound sites. Rust causes powdery brown pustules on leaves, flowers and fruits. Management strategies include removing diseased plants, crop rotation, irrigation practices, fungicide application, and resistant varieties.
The document describes anthracnose, a fungal disease of sorghum caused by Colletotrichum graminicola. Symptoms include leaf spots with white centers surrounded by red, purple, or brown margins and small black lesions. The fungus can also cause stalk rot, seen as discoloration and circular cankers. It is spread primarily through seed and wind-borne spores. Management strategies include destroying plant debris, crop rotation, growing resistant varieties, seed treatment, and fungicide spraying.
Disease and Insect Pest of Ber and their ManagementRamkumarrai3
Ber (Zizyphus Spp.) is a most important fruit and more nutritive value for the purpose of dryland fruit production. Its require heavy pruning during April- May .
This document summarizes the tip over disease of banana, caused by the bacteria Pectobacterium carotovorum sub sp. carotovorum. The disease causes soft rotting of the rhizome and suckers, resulting in stunted growth and yellow leaves. In severe cases, the whole plant can topple over. It is prevalent in areas with hot, damp conditions. Management strategies include using disease-free suckers, removing infected plants, and applying chemicals like copper oxychloride or antibiotics to disinfect the soil.
This document summarizes three major diseases that affect gram (chickpea) crops: wilt, grey mould, and ascochyta blight. It describes the symptoms, causal pathogens, and disease cycles. For wilt, the symptoms include yellowing, wilting, and death of plants. It is caused by Fusarium oxysporum and spreads through soil and irrigation water. For grey mould, symptoms include flower and pod rotting. It is caused by Botrytis cineria and spreads rapidly under humid conditions. For ascochyta blight, symptoms include leaf spots and stem lesions. It is caused by Ascochyta rabiei and spreads through infected plant debris and
This ppt will help Agricultural professionals to diagnose banana diseases and the management strategies. This is a compilation of important diseases of banana prevalent in India which contains some of my own photographs and others collected from Web. This is intended only for educating students and other agricultural field staff.
This document summarizes various pests that affect Gerbera plants, including whiteflies, leaf miners, mites, aphids, and thrips. It describes the appearance and damage caused by each pest, as well as control measures to deal with infestations. Some common control strategies across pests include using biological controls like ladybugs or wasps, removing infested leaves, applying insecticidal soaps or oils, and addressing weeds and other potential pest habitats near the plants. Heavy infestations can stunt plant growth, damage leaves and flowers, and reduce crop yields.
This document summarizes information about wheat yellow stripe rust, caused by the fungus Puccinia striiformis var. tritici. It describes the pathogen's characteristics, life cycle, symptoms, favorable conditions, distribution, and management strategies. Stripe rust is most common in cooler climates and higher elevations. It can cause losses up to 100% but is typically less significant than other wheat rusts. Management involves crop rotation, resistant varieties, fungicide application, and cultural practices like mixed cropping and fertilizer management.
The overall description of major diseases of Rice or Paddy crop is ellustrated in presentation. The students prepairing for Agriculture can feel helpful. Thank You!
People first counted using their fingers and stones. Later, mathematician John Napier invented calculating rods called Napier bones to help with multiplication. A young French mathematician named Blaise Pascal invented an early adding machine while working in his father's office adding tax columns, making his work faster.
The document describes post flowering stalk rot in maize, which is caused by the pathogens Cephalosporium acremonium and Cephalosporium maydis. The pathogens survive in soil, plant debris, and seed. Primary infection occurs through seed, while secondary infection spreads through wind. High temperatures and drought stress favor disease development. Management strategies include crop sanitation, crop rotation, avoiding water stress at flowering, seed treatment, and growing resistant varieties.
Prayers and sacrifices to gods for control of plant diseases
The mid-1600s, a species or variety was reported to be more resistant to a disease than another related species or variety.
Selection of resistant plants as a control of plant diseases.
This is likely to have occurred not only because seeds from resistant and therefore healthier plants looked bigger and better than those from infected susceptible plants, but also because in severe disease out breaks, resistant plants were the only ones surviving and, therefore, their seeds were the only ones available for planting.
Integrated disease management Maize diseases hema latha
This document discusses several diseases that affect maize crops. It begins by introducing maize as a major cereal crop in India and its economic importance. It then describes the major and sporadic diseases that affect maize, when they typically occur, and their potential yield losses. Several diseases are explained in more detail, including their symptoms, disease cycle, distribution, and management strategies. These include turcicum leaf blight, sorghum downy mildew, crazy top of corn, brown stripe downy mildew, and others. Management involves practices like using resistant varieties, crop rotation, removing debris, and fungicide application.
This document summarizes several diseases that affect apples:
1. Apple scab, caused by the fungus Venturia inaequalis, causes black spots on leaves and fruits. Spores are spread by wind and rain. Management includes clean cultivation, resistant varieties, and fungicide sprays.
2. Powdery mildew, caused by Podosphaera leucotricha, produces white or gray powdery patches on leaves, twigs, and fruits. Spores are wind-borne. Management includes sanitation, pre-bloom lime sulfur sprays, and resistant varieties.
3. Fire blight, caused by bacterium Erwinia amylovora, affects blossoms, shoots, branches
Diseases of Onion and garlic and their managementVAKALIYA MUSTUFA
This document provides information on diseases that affect onion and garlic crops and their management. It discusses several fungal diseases including downy mildew, purple blotch, stemphylium blight, basal rot/bulb rot, and rust. For each disease, it describes the symptoms, disease cycle, favorable conditions for development, and recommendations for management through cultural practices and fungicide applications. The overall document aims to review the major diseases of onion and garlic and provide strategies to control them.
The document describes two diseases that affect finger millet: blast and leaf spot. Blast is caused by Pyricularia oryzae and symptoms include brown spots on leaves that enlarge and cause foliage drying. It also causes neck blast where the neck turns black. Leaf spot is caused by Helminthosporium nodulosum and symptoms include small brown oval lesions on young leaves that coalesce into large patches and cause blighting. Both diseases can infect plants at any growth stage and cause yield losses. Management includes seed treatment and foliar fungicide applications.
This document discusses three important diseases of mango: powdery mildew, anthracnose, and mango malformation. It provides details on the symptoms, causal fungi, and favorable conditions for each disease. It also outlines management strategies for each disease, including cultural practices like pruning and spacing, resistant varieties, and fungicide application timings and active ingredients. Key information includes that powdery mildew can cause up to 80% crop loss, anthracnose impacts both pre-and post-harvest fruit, and malformation distorts flowers and shoots.
The document summarizes several diseases that affect marigold plants and their control methods. It describes diseases such as damping off caused by Rhizoctonia solani, leaf spots and blight caused by Alternaria, Cercospora and Septoria species, inflorescence blight caused by Alternaria zinnae, flower bud rot caused by Alternaria dianthi, and powdery mildew caused by Oidium sp. and Leveillula taurica. It provides details on symptoms, causal organisms, and recommendations for control which include soil drenching, fungicide spraying, and dusting with sulfur powder.
This document discusses the anthracnose disease that affects mango plants. It is caused by the fungus Colletotrichum gloeosporioides. The disease causes lesions on leaves, stems, flowers and fruits of mango plants. It thrives under warm and humid conditions between 24-32°C. The fungus overwinters on infected plant debris. Spores are spread by rain splash and irrigation water. Management involves spraying carbendazim during flowering and copper or mancozeb fungicides on leaves at 15 day intervals to control the disease.
This document summarizes bacterial wilt of brinjal, caused by the pathogen Ralstonia solanacearum. It affects eggplant as well as other crops like tomato and potato. Symptoms include sudden wilting and yellowing of leaves. The bacteria enter through wounds in roots and spread through the xylem, blocking water transport. Warm, moist conditions favor disease development. Management strategies include removing infected plants, crop rotation, and using resistant varieties.
This document discusses pomegranate production, post-harvest management techniques, and value addition opportunities for pomegranates. It provides an overview of pomegranate production levels in India and discusses the various health benefits of pomegranates. It then outlines several unit operations for post-harvest handling of fruits and vegetables including harvesting, cooling, storage, and transportation. Various techniques for pomegranate processing are described including aril extraction, minimal processing, and development of products like anardana powder, jelly, and tablets. Storage and cooling technologies like evaporative cool chambers and a two-stage evaporative cooler are also summarized. Finally, the document discusses entrepreneurship and training programs offered by the Central Institute of Post
This document discusses five common diseases that affect safflower: Alternaria blight, Sclerotinia head rot, root rot, gray mold, and rust. Alternaria blight causes dark lesions on stems and leaves and can rot seeds. Sclerotinia head rot causes heads to disintegrate, leaving sclerotia. Root rot causes wilting and bleaching as roots and stems rot. Gray mold causes soft rotting at wound sites. Rust causes powdery brown pustules on leaves, flowers and fruits. Management strategies include removing diseased plants, crop rotation, irrigation practices, fungicide application, and resistant varieties.
The document describes anthracnose, a fungal disease of sorghum caused by Colletotrichum graminicola. Symptoms include leaf spots with white centers surrounded by red, purple, or brown margins and small black lesions. The fungus can also cause stalk rot, seen as discoloration and circular cankers. It is spread primarily through seed and wind-borne spores. Management strategies include destroying plant debris, crop rotation, growing resistant varieties, seed treatment, and fungicide spraying.
Disease and Insect Pest of Ber and their ManagementRamkumarrai3
Ber (Zizyphus Spp.) is a most important fruit and more nutritive value for the purpose of dryland fruit production. Its require heavy pruning during April- May .
This document summarizes the tip over disease of banana, caused by the bacteria Pectobacterium carotovorum sub sp. carotovorum. The disease causes soft rotting of the rhizome and suckers, resulting in stunted growth and yellow leaves. In severe cases, the whole plant can topple over. It is prevalent in areas with hot, damp conditions. Management strategies include using disease-free suckers, removing infected plants, and applying chemicals like copper oxychloride or antibiotics to disinfect the soil.
This document summarizes three major diseases that affect gram (chickpea) crops: wilt, grey mould, and ascochyta blight. It describes the symptoms, causal pathogens, and disease cycles. For wilt, the symptoms include yellowing, wilting, and death of plants. It is caused by Fusarium oxysporum and spreads through soil and irrigation water. For grey mould, symptoms include flower and pod rotting. It is caused by Botrytis cineria and spreads rapidly under humid conditions. For ascochyta blight, symptoms include leaf spots and stem lesions. It is caused by Ascochyta rabiei and spreads through infected plant debris and
This ppt will help Agricultural professionals to diagnose banana diseases and the management strategies. This is a compilation of important diseases of banana prevalent in India which contains some of my own photographs and others collected from Web. This is intended only for educating students and other agricultural field staff.
This document summarizes various pests that affect Gerbera plants, including whiteflies, leaf miners, mites, aphids, and thrips. It describes the appearance and damage caused by each pest, as well as control measures to deal with infestations. Some common control strategies across pests include using biological controls like ladybugs or wasps, removing infested leaves, applying insecticidal soaps or oils, and addressing weeds and other potential pest habitats near the plants. Heavy infestations can stunt plant growth, damage leaves and flowers, and reduce crop yields.
This document summarizes information about wheat yellow stripe rust, caused by the fungus Puccinia striiformis var. tritici. It describes the pathogen's characteristics, life cycle, symptoms, favorable conditions, distribution, and management strategies. Stripe rust is most common in cooler climates and higher elevations. It can cause losses up to 100% but is typically less significant than other wheat rusts. Management involves crop rotation, resistant varieties, fungicide application, and cultural practices like mixed cropping and fertilizer management.
The overall description of major diseases of Rice or Paddy crop is ellustrated in presentation. The students prepairing for Agriculture can feel helpful. Thank You!
People first counted using their fingers and stones. Later, mathematician John Napier invented calculating rods called Napier bones to help with multiplication. A young French mathematician named Blaise Pascal invented an early adding machine while working in his father's office adding tax columns, making his work faster.
This document contains biographical information about multiple individuals who passed away between 1980 and 2013. It includes their dates of birth and death, ages, affiliations, occupations, and in some cases brief details about their interests and families. The individuals commemorated ranged in age from 18 to 50 years old at the time of their passing. Many were noted to be graduates of or affiliated with local schools and churches in the Johnstown, Pennsylvania area.
This short document promotes creating presentations using Haiku Deck on SlideShare. It encourages the reader to get started making their own Haiku Deck presentation by providing a button to click to begin the process. The document is advertising the creation of presentations on Haiku Deck and SlideShare.
Sarah Alvis is a junior who enjoys reading and some activities. She states that she likes reading, breathing, and some stuff, though something else may be better. Additionally, she notes that she is American.
This document introduces a Multi-Dimensional Index of Nutrition (NeSNI) to better communicate the complexity of nutrition issues to policymakers. The NeSNI ranks countries based on 6 nutrition indicators: stunting, anemia, low birth weight, childhood overweight, exclusive breastfeeding, and wasting. It shows how countries perform on the overall index and individual indicators. The document discusses how the NeSNI can help track progress towards global nutrition targets and clarify how different policy areas and sectors like agriculture can impact nutrition outcomes.
Ash Ketchum is the main character from the Pokémon cartoon series. He wears the same clothes in every season and is always accompanied by his Pokémon partner Pikachu. Ash travels around different regions challenging gym leaders and collecting badges by battling with his Pokémon. He maintains a consistently positive attitude even when facing challenges and encourages others to keep trying. Other characters see Ash as a skilled trainer and good friend.
Cleveland Brown is a family man from Stoolbend, Virginia who works and eats a lot. He has a mustache, wears a yellow shirt and blue pants, and says funny things. His son looks up to him, though he does not think very much himself.
The document introduces the Tizen SDK, which provides a comprehensive set of tools for developing Tizen applications. The SDK includes an IDE, emulators, debugging tools, and supports development of both web and native applications. It aims to be specialized for Tizen, support multiple devices and hosts, provide an all-in-one development environment, and offer rich features. The SDK is based on open technologies and its architecture is extensible to customize the Tizen platform.
Bubbles is one of the Power Puff Girls who lives in Townsville, USA. She has blonde pigtails, big blue eyes, pale skin, and wears a blue dress with white leggings and black shoes while fighting monsters and protecting the city with her sisters. Bubbles thinks often about her stuffed octopus and her sisters' safety, loves animals, and can emit supersonic waves from her voice.
IRJET- Disease Detection in the Leaves of Multiple PlantsIRJET Journal
1. The document proposes a deep learning-based method to detect diseases affecting the leaves of multiple plant varieties.
2. A convolutional neural network (CNN) model called ResNet-50 is trained on 1,222 images of diseased plant leaves representing 5 different diseases.
3. The CNN model achieves over 96% classification accuracy, outperforming conventional machine learning techniques for disease detection.
This document presents a proposed system for disease diagnosis of mango leaves using image processing techniques. The system uses a three-step process: 1) Image analysis to preprocess leaf images and extract affected regions, 2) Feature extraction of color and texture characteristics from affected regions, 3) Classification of leaf diseases based on extracted features using a trained machine learning model. The proposed system is intended to help agricultural specialists and farmers diagnose leaf diseases early and accurately by analyzing digital images of affected leaves. Some key advantages of the system include being low-cost, time-efficient, and able to diagnose multiple diseases. The system was tested on a dataset of 129 mango leaf images with promising 89.92% accuracy in identifying three common mango diseases.
Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
RICE PLANT DISEASE DETECTION AND REMEDIES RECOMMENDATION USING MACHINE LEARNINGIRJET Journal
This document describes a machine learning approach to detect diseases in rice plants from images and recommend remedies. It discusses three common rice diseases - leaf blast, bacterial leaf blight, and hispa - and how a convolutional neural network was trained on thousands of images to classify diseases. The proposed method uses CNN layers to extract features from images and fully connected layers to classify diseases. It aims to help farmers early detect diseases from photos and provide effective treatment recommendations to improve crop yields.
Image based anthracnose and red-rust leaf disease detection using deep learningTELKOMNIKA JOURNAL
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease’s detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from “ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101” show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease’s detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
A study on real time plant disease diagonsis systemIJARIIT
The document discusses developing a real-time plant disease diagnosis mobile application. It aims to allow farmers to easily capture images of plant leaves using a mobile camera, send the images to a central system for analysis, and receive diagnoses and treatment recommendations. The proposed system would use image processing and data mining techniques to analyze leaf images for abnormalities, identify the plant species, recognize any diseases present, and recommend appropriate pesticides and estimate treatment costs. This would provide a low-cost, convenient solution for farmers to quickly diagnose and respond to plant diseases.
POMDETECT: AN INVESTIGATION INTO LEAF DISEASE DETECTION TECHNIQUESIRJET Journal
This document discusses various techniques for detecting diseases in pomegranate leaves, including visual inspection, spectral imaging, and machine learning approaches. It analyzes several studies that evaluated these techniques and their effectiveness in detecting common diseases like bacterial blight, anthracnose, and powdery mildew. Machine learning techniques like convolutional neural networks were shown to outperform other methods in accuracy and speed of detection. The document highlights the potential of these techniques, especially deep learning, to develop automated disease monitoring systems and aid farmers in managing diseases.
POMDETECT: AN INVESTIGATION INTO LEAF DISEASE DETECTION TECHNIQUESIRJET Journal
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Opportunity scholarships and the schools that receive them
Pomegranate fuzzy
1. Grading & Identification of Disease in
Pomegranate Leaf and Fruit
Tejal Deshpande1
, Sharmila Sengupta2
, K.S.Raghuvanshi 3
1
Assistant professor, Electronics & Telecommunication, Xavier Institute of Engg
2
Assistant professor, Electronics & Telecommunication, Vivekanand Institute of Technology
1,2
University of Mumbai, India
3
Associate professor, Plant pathology, Mahatma Phule Krishi Vidyapeeth
3
Mahatma Phule agriculture university ,Rahuri, Ahmednagar India
Abstract - Present paper is an attempt to automatically grade
the disease on the Pomegranate plant leaves. This innovative
technique would be a boon to many and would have a lot of
advantages over the traditional method of grading. There has
been a sea change in the mindset and the effort put down by
the agricultural industry by adapting to the current trends &
technologies. One such example is the use of Information and
Communication Technology (ICT) in agriculture which
eventually contributes to Precision Agriculture. Presently,
plant pathologists follow a tedious technique that mainly relies
on naked eye prediction and a disease scoring scale to grade
the disease. Manual grading is not only time consuming but
also does not give precise results. Hence the current paper
proposes an image processing methodology to deal with one of
the main issues of plant pathology i.e disease grading. The
results are proved to be accurate and satisfactory in contrast
to manual grading and hopefully take a strong leap forward in
establishing itself in the market as one of the most efficient and
effective process.
The proposed system is also an efficient module that identifies
the Bacterial Blight disease on pomegranate plant. At first, the
captured images are processed for enhancement. Then image
segmentation is carried out to get target regions (disease spots)
on the leaves and fruits. Later, if the diseased spot on leaf is
bordered by yellow margin then it is said that leaf is infected
by bacterial blight otherwise not. Similarly when black spots
are targeted on fruits, they are checked for whether a crack is
passing through these spots. If cracks are passing through the
spots then the disease identified would be Bacterial blight.
Based on these two characteristics bacterial blight on
pomegranate can be appropriately identified.
Keywords— Percent Infection, Bacterial Blight, K-means
clustering, Morphology, colour image segmentation, Precision
agriculture.
I. INTRODUCTION
Sole area that serves the food needs of the entire human
race is the Agriculture sector. Research in agriculture is
aimed towards increase of productivity and food quality at
reduced expenditure and with increased profit [1]. In the
past few years new trends have emerged in the agricultural
sector. Due to the manifestation and developments in the
fields of sensor networks, robotics, GPS technology,
communication systems etc, precision agriculture started
emerging [2]. Precision agriculture concentrates on
providing the means for observing, assessing and
controlling agricultural practices. It also takes into account
the pre- and post-production aspects of agricultural
enterprises. The objectives of precision agriculture are
profit maximization, agricultural input rationalization and
environmental damage reduction, by adjusting the
agricultural practices to the site demands. The challenge of
the precision approach is to equip the farmer with adequate
and affordable information and control technology.
Plant disease is one of the crucial causes that reduces
quantity and degrades quality of the agricultural products.
Disease is impairment to the normal state of the plant that
modifies or interrupts its vital functions such as
photosynthesis, transpiration, pollination, fertilization,
germination etc. The emergence of plant diseases has
become more common now days, as factors such as
climate and environmental conditions are more unsettled
than ever [2]. Plant diseases are usually caused by fungi,
bacteria and viruses. Also there are other diseases which
are caused by adverse environmental conditions. There are
numerous characteristics and behaviours of such plant
diseases in which many of them are merely
distinguishable. The ability of disease diagnosis in earlier
stage is an important task. Hence an intelligent decision
support system for Prevention and Control of plant
diseases is needed. This system uses some high-tech and
practical technology to appropriately detect and diagnose
the plant diseases. Technological advancement is
gradually finding its applications in the field of agriculture
[3]. The information and communication technology (ICT)
application is going to be implemented as a solution in
improving the status of the agricultural sector [4]. The idea
of integrating ICT with agriculture sector motivates the
development of an automated system for pomegranate
disease classification and its grading.
Pomegranate (Punica granatum), so called “fruit of
paradise” is one of the major fruit crops of arid region. It is
Popular in Eastern as well as Western parts of the world.
The fruit is grown for its attractive, juicy, sweet-acidic and
fully luscious grains called ‘Arils’ [6]. The fruits are
mainly used for dessert purposes. In India it is cultivated
over the area of about 63,000 ha, and its production is
about 5 lakh tons/annum. Important varieties cultivated are
Ganesh, Dholka, Seedless(Bedana), Bhagwa, Araktha.
Figure1 shows three varieties of pomegranate fruit.
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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2. a) Bhagwa (b) Ganesh (c) Araktha
Figure1- Varieties of pomegranate
Based on size and colour, pomegranate fruits are graded as
follows:
Super size- in which fruits are free from spots and
individual fruit weight is more than750grams.
King size –in which fruits are attractive and individual
fruit weight is 500-700 grams.
Queen size- in which fruits are attractive, red and
individual fruit weight is 400-500 grams.
Prince size- in which fruits are attractive, red and
individual fruit weight is 300-400 grams.
Unfortunately there are no organized marketing systems
for pomegranate. Usually farmers dispose their produce to
contractors who will later transport too far off markets.
There is scope for exporting Indian pomegranates to
Bangladesh, Bahrain, Canada, Germany, United Kingdom,
Japan, Kuwait, Sri Lanka, Omen, Pakistan, Qatar, Saudi
Arabia, Singapore, Switzerland, U.A.E. and U.S.A.
Diseases and insect pests are the major problems that
threaten pomegranate cultivation. These require careful
diagnosis and timely handling to protect the crops from
heavy losses [6]. In pomegranate plant, diseases can be
found in various parts such as fruit, stem and leaves. Major
diseases that affect pomegranate fruit are bacterial blight
(Xanthomonas axonopodis pv punicae), antracnose
(Colletotrichum gloeosporoides) and wilt complex
(ceratocystis fimbriata).
Image samples of these diseases are shown in Figure 2.
Wilt Complex
Anthracnose
Scab
Bacterial Blight on fruit
Bacterial Blight on leaf
Bacterial Blight on Stem
Figure2- Various diseases affecting pomegranate
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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3. Bacterial blight is the most severe disease of the
pomegranate. The disease symptoms can be initially found
on stem part which gradually pervades to leaves and then to
fruits. On stem, the disease starts as brown to black spot
around the nodes. In advance stages of nodal infection
girdling and cracking of nodes lead to break down of
branches. On leaves, the disease starts with small, irregular,
water soaked spots that are 2 to 5 mm in size with necrotic
centre of pin head size. Spots are translucent against light.
Later, these spots turn light to dark brown and are
surrounded by prominent yellow margin. Numerous spots
may coalesce to form bigger patches. Severely infected
leaves may drop off. On fruits brown to black spots appear
on pericap with cracks passing through the spots. The
disease spreads as the bacterium survives on the tree as well
as in diseased fallen leaves. High temperature and high
relative humidity favours the disease. The disease spreads
to healthy plants through wind splashed rains and in new
area through infected cuttings.
II. MANUAL GRADING : PRESENT APPROACH
Plants are bound to have diseases. The infected plants are
diagnosed and treatment is suggested to cure the disease. To
treat the disease chemical pesticides are used. Pesticides are
substances or mixture of substances intended for
preventing, destroying, repelling or mitigating any pest.
Chemicals are continually becoming a more intricate part of
modern society. The rampant use of these chemicals, under
the adage, “if little is good, a lot more will be better” has
played havoc with human and also on agricultural products.
Use of these toxic chemicals can only be minimised when
the disease is identified accurately along with the stage in
which the disease is observed.
Presently, the plant pathologists rely on disease scoring
scale to grade the disease. This is shown in Table 1.
Table 1: Disease Scoring Scale for Leaves
From table 1, it is observed that the grade of the disease is
assigned based on the percent-infection i.e., if the infection
percent is about 5 then the grade is 2. The problem here is
that even if the percent-infection is 2 or 9 the grade remains
2 only.
Presently, percent-infection is calculated based on grid
paper analysis. This method is time consuming and burden
of repetitive tasks. Since human intervention is involved, it
is prone to errors also. Hence, now the time has come to
overcome these problems. This can be achieved by
inculcating machine vision into the agriculture to get the
accurate grade of the disease. With this motto the present
paper proposes an automatic and accurate disease grading
system for plant leaves which can be of great use for the
agronomists. For the experimentation purpose,
pomegranate leaves are considered.
III. METHODOLOGY
Proposed method will grade and identify Bacterial Blight
disease of pomegranate leaf and fruit.
The system architecture is presented in figure2.
The system is divided into the following steps: (1) Image
acquisition (2) Image Pre-processing (3) Colour image
segmentation (4) Calculating AT and AD (5) Disease
grading
A. System Architecture
Figure2: System architecture
B. Image Acquisition
First stage of any vision system is the image acquisition
stage. The digitization and storage of an image is referred as
the image acquisition. After the image has been obtained,
various methods of processing can be applied to the image
to perform the many different vision tasks required today.
However, if the image has not been acquired satisfactorily
then the intended tasks may not be achievable, even with
the aid of some form of image enhancement.
All the images are saved in the JPEG format. For the
purpose of image acquisition, author has visited and
captured images from several pomegranate farms in Rahuri,
Ahmednagar district, Maharashtra, India.
C. Image Pre-Processing
Pre-processing images commonly involves removing low-
frequency background noise, normalizing the intensity of
the individual particles images, removing reflections, and
masking portions of images[1]. Image pre-processing is the
technique of enhancing data images prior to computational
processing.
Image processing is a form of signal processing for which
the input is an image, such as a photograph or video frame;
the output of image processing may be either an image or a
set of characteristics or parameters related to the image [1].
Pre-processing uses the techniques such as image resize,
erosion, dilation, segmentation, cropping, etc.
Initially, captured images are resized to a fixed resolution
so as to utilize the storage capacity or to reduce the
computational burden in the later processing. Shadow may
or not be there image acquisition. Shadow would disturb the
segmentation and the feature extraction of disease spots. So
it must be removed or weakened before any further image
Percent Infection Disease Grade
0 – 1 1
1-10 2
10-20 3
20-40 4
40-100 5
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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4. analysis by applying shadow removal algorithms which
makes use of morphology. In the present work, author has
considered erosion, dilation operations for getting better
results.
D. Image Segmentation
Image segmentation refers to the process of partitioning the
digital image into its constituent regions or objects so as to
change the representation of the image into something that
is more meaningful and easier to analyze. The level to
which the partitioning is carried depends on the problem
being solved i.e. segmentation should stop when the objects
of interest in an application have been isolated [5]. In the
current work, the very purpose of segmentation is to
identify regions in the image that are likely to qualify as
diseased regions. There are various techniques for image
segmentation. K-means clustering method has been used in
the present work to carry out segmentation. K-Means
Clustering is a method of cluster analysis which aims to
partition n observations into k mutually exclusive clusters in
which each observation belongs to the cluster with the
nearest mean.
When the segmentation is completed, one of the clusters
contains the diseased spots being extracted. This image is
saved and considered for calculating AD.
E. Calculating AT and AD
In image processing terminology area of a binary image is
the total number of on pixels in the image[1]. Hence, the
original resized image is converted to binary image such
that the pixels corresponding to the leaf image are on. From
this image total leaf area (AT) is calculated. Similarly, the
output image from color image segmentation, containing
the disease spots, is used to calculate total disease area
(AD)[1].
F. Disease Grading
Once AT and AD are known, the percent-infection (PI) is
calculated by applying the formula (1).
PI= (AD / AT ) *100 . . . (1)
Grade of the disease has to be determined from PI.
Percent Infection Disease Grade
0 0
0.1-5.999 1
6-15.999 2
16-25.999 3
26-35.999 4
36-45.999 5
46-55.999 6
56-65.999 7
66-75.999 8
76-100 9
Table2: Disease Scoring Scale for Leaves
Grading Process is shown in figure3.
G. Disease Detection
Image Pre-processing is carried out on the acquired image.
Shadow removal and image correction algorithm are used
in this stage. For removing shadow morphology
fundamentals such as erosion and dilation are used. In
image post processing the interested part is extracted by
using K-Means clustering and its features are analyzed. If
the leaf containing brown to black spot is bordered by
yellow margin, denotes that the leaf is infected by Bacterial
Blight. For fruits first the black spots are identified and if
there is a crack passing through that black spot it signifies
that the fruit is infected by Bacterial Blight. Cracks are
found using canny edge detector.
Once the disease is identified, grading is done so that
appropriate treatment advisory can be provided by seeking
the help from agricultural experts so that the disease can be
prevented from further spreading.
Disease detection process is shown in figure4.
IV. DESIGN
A. Flowchart for Grading
Figure3: Grading Process
Start
Image Acquisition
Image Resize
Image Pre-processing
(Image correction,
Shadow Removal)
Colour Image
Segmentation (KMeans)
Clustering)
Diseased spot extracted
C
C
Calculate Disease Area
(Ad)
Covert the original
image to B/W image
Calculate Total Area
(At)
Percent Infection
PI= ( Ad / At)*100
Disease Grading
Stop
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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5. B. Disease Detection Process
Figure4: Disease detection Process
V. RESULTS
A. Image Acquisition
Figure 5 shows the images of pomegranate leaf diseased by
Bacterial Blight.
Sample 1
Sample 2
Sample 3
Figure5: samples of Leaf Diseased by Bacterial Blight
Start
Image Acquisition
Image Resize
Image Preprocessing
(Image correction,
Shadow Removal)
Color Image
Segmentation (KMeans
Clustering)
Diseased spot extracted
If Leaf?
Yes
No
If Fruit?
Yellow
margin around
diseased area?
Crack
through
black spot?
Yes
No
No
Bacterial blight on
leaf
Good Leaf /
wrong result
Bacterial blight
on fruit
No
Good fruit /
wrong result
Yes
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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6. B. Image Pre-processing
Resize
The image is resized to a resolution of
[250 300].
Morphology
Operations like erosion and dilation are used. Shadow
removal algorithms are applied to the images wherever
necessary.
C. Colour image segmentation
K-means segmentation algorithm requires users to select
the value ‘k’. The correct choice of k is often ambiguous.
Increasing k will always reduce the amount of error in the
resulting clustering, to the extreme case of zero error if each
data point is considered its own cluster (i.e., when k equals
the number of data points, n). Intuitively then, the optimal
choice of k will strike a balance between maximum
compression of the data using a single cluster, and
maximum accuracy by assigning each data point to its own
cluster.
After some trial and error method, for the current work,
value of K is chosen as 2.
D. Calculating AT and AD
Figure6 shows the binary images of the original resized
image.
Sample1
Sample 2
Sample 3
Figure6: Black and white images of the different query
image
Figure7 shows the images of the diseased portion.
Sample1
Sample 2
Sample 3
Figure 7: Diseased portion of the different query image
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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7. E. Disease grading
From (1), Percent-infection is given by
PI= (AD / AT) *100
= ( 72990/ 178334 ) * 100
= 40.9288 %
Sample 1
Sample 2
Sample 3
Figure8: Grading Results of different Query images
A System is developed for disease grading by referring to
the disease scoring scale in Table1 to grade the disease.
Figure8 shows the result of grading for different images
From the result, it can be observed that the accurate values
of percent-infection and disease grade are obtained with
which a proper treatment advisory can be given thereby
eliminating the above mentioned problems. Also chemical
spray frequency can be minimised thereby reducing
chemical residue in plant parts i.e. food or fodder parts.
F. Disease Detection Results:
Edge Detection algorithms have been used along with black
spot detection algorithm.
Figure9: Leaf Disease Detection for un-diseased leaf image
Figure10: Leaf Disease Detection for diseased leaf image
Figure11: fruit disease identification for un-diseased fruit
image
Figure12: fruit disease identification for diseased fruit
image
Tejal Deshpande et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4638-4645
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