This document discusses analyzing the cost of crop production and forecasting crop prices. It first discusses how various technologies like IoT, AI, and drones have been used to automate agriculture by helping with tasks like weather forecasting, inspecting crop quality, and pesticide spraying. However, finance management is still a major issue for farmers.
The document then reviews previous research on forecasting crop yields and prices. It finds that most studies only focus on specific crops in certain regions, and there is no global mechanism for price forecasting. The document also discusses how crop production costs are calculated, finding that current methods don't consider important variables like soil properties.
The document proposes using image processing and sensors to automatically measure soil
IRJET- Price Forecasting System for Crops at the Time of SowingIRJET Journal
1. The document proposes a price forecasting system for crops in India that uses past price data and a ARIMA (Auto Regressive Integrated Moving Average) model for time series analysis.
2. It analyzes factors like weather, soil conditions, production levels that affect crop prices. The proposed system would predict prices at the time of sowing based on these factors.
3. Preliminary results show the ARIMA model has potential to predict crop prices up to 95% accuracy and will improve with more daily price data. Accurate forecasts could help farmers and policymakers.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
““Smart Crop Prediction System and Farm Monitoring System for Smart Farming””IRJET Journal
This document presents a smart crop prediction and farm monitoring system that uses machine learning and IoT technologies. The system aims to help farmers select suitable crops based on soil type and climate conditions. It analyzes data on soil properties, temperature, moisture and humidity to predict crop growth. It also develops a module for remote farm monitoring using sensors and a camera. The system is intended to guide farmers, especially small-scale farmers, in cultivating crops according to soil and weather conditions. It also notifies farmers if animals enter the farm or if the soil moisture level requires irrigation. The system uses techniques like CNN for crop prediction based on soil images and sends SMS alerts to farmers.
Famer assistant and crop recommendation systemIRJET Journal
This document describes a farmer assistant and crop recommendation system developed by researchers in India. The system uses machine learning algorithms to analyze soil quality parameters and weather data to recommend profitable crops for farmers to plant based on their location. It also predicts the best time and place for farmers to sell their crops to maximize profits. The system is designed as a web application that farmers can access using smartphones, providing crop recommendations, pricing information, and other services to help farmers increase yields and earnings. The researchers used an XGBoost classifier trained on agricultural data to predict soil fertility and suitable crops. The system aims to help farmers get higher profits through improved agricultural decision making.
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
IRJET- Price Forecasting System for Crops at the Time of SowingIRJET Journal
1. The document proposes a price forecasting system for crops in India that uses past price data and a ARIMA (Auto Regressive Integrated Moving Average) model for time series analysis.
2. It analyzes factors like weather, soil conditions, production levels that affect crop prices. The proposed system would predict prices at the time of sowing based on these factors.
3. Preliminary results show the ARIMA model has potential to predict crop prices up to 95% accuracy and will improve with more daily price data. Accurate forecasts could help farmers and policymakers.
This document summarizes different analytical models that can be used for crop prediction, including classification models. It discusses feature selection methods like principal component analysis and information gain that are important for crop prediction models. The document reviews different machine learning techniques used in previous studies for crop yield prediction, such as linear regression, k-nearest neighbors, neural networks, support vector machines, and decision trees. It aims to compare the performance of these classification techniques for predicting crop yields based on parameters like temperature, rainfall, soil characteristics, and more.
IMPLEMENTATION PAPER ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document presents an implementation paper on an agriculture advisory system that uses machine learning algorithms to predict optimal crops and recommend fertilizers. It first reviews previous literature on similar crop prediction systems using data mining techniques. It then describes the proposed system's methodology, which involves 8 steps: data collection, preprocessing, training, supervised learning using Naive Bayes for crop prediction and KNN for fertilizer recommendation, priority-based crop recommendation, location- and year-based recommendations, output of results, and visual representation of recommendations. The system aims to help farmers select profitable crops and increase agricultural output by providing customized recommendations based on soil analysis and other input data. It concludes the proposed system could help address issues farmers face by streamlining information and facilitating efficient
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
Crop Selection Method Based on Various Environmental Factors Using Machine Le...IRJET Journal
This document proposes two crop selection methods using machine learning:
1. A Crop Selection Method that uses classification algorithms to select the most suitable crop based on environmental and economic factors like temperature, rainfall, soil type, and market prices.
2. A Crop Sequencing Method that uses a crop sequencing algorithm to suggest an optimal sequence of crops over a growing season based on predicted yield rates and market prices to maximize profits. Both methods use a machine learning tool called WEKA and historical crop data to make predictions.
““Smart Crop Prediction System and Farm Monitoring System for Smart Farming””IRJET Journal
This document presents a smart crop prediction and farm monitoring system that uses machine learning and IoT technologies. The system aims to help farmers select suitable crops based on soil type and climate conditions. It analyzes data on soil properties, temperature, moisture and humidity to predict crop growth. It also develops a module for remote farm monitoring using sensors and a camera. The system is intended to guide farmers, especially small-scale farmers, in cultivating crops according to soil and weather conditions. It also notifies farmers if animals enter the farm or if the soil moisture level requires irrigation. The system uses techniques like CNN for crop prediction based on soil images and sends SMS alerts to farmers.
Famer assistant and crop recommendation systemIRJET Journal
This document describes a farmer assistant and crop recommendation system developed by researchers in India. The system uses machine learning algorithms to analyze soil quality parameters and weather data to recommend profitable crops for farmers to plant based on their location. It also predicts the best time and place for farmers to sell their crops to maximize profits. The system is designed as a web application that farmers can access using smartphones, providing crop recommendations, pricing information, and other services to help farmers increase yields and earnings. The researchers used an XGBoost classifier trained on agricultural data to predict soil fertility and suitable crops. The system aims to help farmers get higher profits through improved agricultural decision making.
IRJET- Agricultural Data Modeling and Yield Forecasting using Data Mining...IRJET Journal
This document proposes using data mining techniques to develop a predictive model for forecasting crop yields. It involves collecting agricultural data on factors like rainfall, temperature, seed quality, and sowing procedures. Data preprocessing and clustering techniques like K-means are applied. Classification algorithms like Support Vector Machine and Naive Bayes are used to predict crop yield as low, medium, or high. The predictive model aims to help farmers plan cultivation for high crop yields by identifying the best combinations of agricultural factors.
IRJET- Survey on Crop Suggestion using Weather AnalysisIRJET Journal
The document discusses a proposed model to predict the most suitable crop for a given location based on weather analysis and soil parameters. It would use fuzzy logic, Gradient Boosted Decision Tree (GBDT) algorithm, and R Neuralnet Package. The model aims to address the problems of crop failure, food shortage, and increasing farmer suicides by recommending crops suited to the climatic conditions and soil quality of a particular site. It would provide suggestions on both crop yield and suitable crop types to maximize agricultural productivity. The inputs to the system would be meteorological and soil data, and it would analyze past and future weather data to recommend crops.
IRJET- Crop Prediction and Disease DetectionIRJET Journal
This document discusses a proposed system for crop prediction and disease detection using data mining techniques and image processing. The system would use algorithms like Apriori and C4.5 to predict crop yields based on past climate data like temperature and rainfall. It would also allow farmers to upload images of crop diseases to identify the disease and recommended treatments. The goal is to help farmers make better decisions around crop selection and disease management given expected climate conditions.
Application Of Machine Learning in Modern Agriculture for Crop Yield Predicti...IRJET Journal
This document proposes a machine learning model for crop yield prediction and fertilizer recommendations in agriculture. It discusses existing systems that focus on single crops or aspects of agriculture. The proposed system predicts crop type, fertilizer type, and fertilizer amount using multiple machine learning algorithms. It finds that stacking XGB and random forest models performs best for crop and fertilizer type prediction. Regression models best predict fertilizer amount, with XGB regression performing best. The system is intended to help farmers plan crops and increase yields. It is evaluated using real-world agricultural data and metrics, finding it can effectively predict crops, fertilizer needs, and amounts to assist modern agriculture.
IRJET- Crop Yield Prediction based on Climatic ParametersIRJET Journal
The document describes a study that developed a machine learning model and web application to predict crop yields based on climatic parameters. The model was trained using a random forest algorithm on historical crop production and climate data from Maharashtra, India. The application allows farmers to input details of their district, crop, and field area to receive a predicted crop yield output. The model achieved 87% accuracy on 10-fold cross validation testing. The goal was to help farmers and policymakers make informed decisions based on predicted yields under varying climate conditions.
IRJET - Analysis of Crop Yield Prediction by using Machine Learning AlgorithmsIRJET Journal
This document analyzes crop yield prediction using machine learning algorithms like K-Nearest Neighbor and Support Vector Machine. It discusses collecting agricultural data from various regions on factors like rainfall, humidity, temperature, area, yield, soil type and location. The data is preprocessed, transformed and split into training and testing sets. Both KNN and SVM are applied to the data and SVM is found to have higher accuracy and faster execution time compared to KNN in predicting suitable crops and estimated yields. The proposed system provides farmers an efficient way to predict crops and yields for their region using modern machine learning techniques.
IRJET- Crop Price Forecasting System using Supervised Machine Learning Al...IRJET Journal
The document proposes a crop price and profit forecasting system that uses machine learning algorithms like Naive Bayes and K-Nearest Neighbors to predict crop prices and profits based on past data and current parameters. The system is intended to help farmers make more informed decisions by providing predictions of crop performance, prices, and profits before planting. It analyzes factors like rainfall, yields, costs and prices in its predictions in order to increase agricultural profits and the country's economy.
Crop Recommendation System Using Machine LearningIRJET Journal
The document describes a machine learning-based crop recommendation system that analyzes soil and climate data to predict the most suitable crops for farmers to grow. It evaluates several machine learning algorithms (decision tree, support vector machine, logistic regression, random forest) and finds that random forest has the highest accuracy at 99.09%. The system is implemented as a website using the random forest model to help farmers select optimal crops.
1. The document discusses the development of a machine learning-based system to provide precise crop yield recommendations to farmers in India.
2. Over 60% of Indians work in agriculture but farmers often grow the same crops without trying new varieties and apply fertilizers inconsistently, affecting yields and soil quality.
3. The proposed system aims to address these issues by recommending the optimal crop for a given plot of land based on soil composition and environmental factors using machine learning algorithms.
Data driven algorithm selection to predict agriculture commodities priceIJECEIAES
Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models.
A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEMIRJET Journal
This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
This document discusses how precision farming and big data can help improve agriculture. It notes that a majority of India's population depends on agriculture but farmers often lack information which can hurt crop yields. New technologies using sensors, cloud computing, and mobile phones can now provide farmers real-time data on soil conditions, weather, and crop health to help maximize production. Data mining techniques like classification and clustering can analyze large agricultural data sets to predict outcomes and identify patterns. This information can help farmers choose optimal crops and growing practices and help businesses anticipate supply and demand trends to better match production and pricing.
IRJET - Enlightening Farmers on Crop YieldIRJET Journal
This document discusses using data mining techniques to predict crop yields to help farmers. It proposes using a random forest regression algorithm on past agricultural data from 2000-2014 to build a prediction model. The model would help farmers select optimal crops, understand weather patterns, and maximize yields. The system is described as gathering data, preprocessing it, training a random forest model on 60% of the data and testing it on 20%. It would then provide yield predictions and recommendations to farmers through a visualization tool. The goal is to help guide farmers' decisions around fertilizer use, soil management, and crop selection to improve production levels.
Smart Farming Using Machine Learning AlgorithmsIRJET Journal
This document discusses using machine learning algorithms for smart farming and crop prediction. It proposes a system that collects sensor data on temperature, soil moisture, and other variables to predict optimal crops for a farm. An artificial neural network (ANN) algorithm is identified as suitable for this application due to its ability to learn from large datasets like those collected from IoT sensors. The system architecture involves sensors collecting data which is sent to a Raspberry Pi for processing and training a predictive model. This model would then provide crop recommendations to farmers to help guide decisions and reduce risks from climate change.
AI Based Smart Agriculture – Leaf Disease Prediction Using Optimized CNN ModelIRJET Journal
This document discusses using optimized convolutional neural network (CNN) models for leaf disease prediction in smart agriculture. Sensors are used to collect environmental data from fields, and images of plant leaves are analyzed for disease identification. Three CNN methods - fast R-CNN, faster R-CNN, and Mask R-CNN - are evaluated and the best method is selected based on prediction accuracy. The optimized CNN model identifies diseases and recommends suitable pesticides, while sensor data is also used for irrigation control and crop recommendations based on soil conditions. The system aims to help farmers detect diseases early and improve crop productivity using artificial intelligence and internet of things technologies.
This document describes a web application called Farm-Easy that aims to help farmers. It discusses:
1) Farm-Easy allows farmers and vendors to register and login. Vendors can update stock prices weekly and farmers can view predicted crop prices.
2) Related works explored e-agriculture platforms, agribusiness e-commerce systems, and different methods for predicting agricultural commodity prices.
3) Farm-Easy's methodology uses PHP and MySQL to develop separate vendor and farmer portals. Vendors update stock prices and farmers can view prices to make informed decisions. Naive Bayes is used to predict crop prices.
Smart Agriculture And Farmer's Assistance System On Machine LearningIRJET Journal
This document proposes a smart agriculture system that uses machine learning algorithms like random forest to help farmers make better decisions. The system collects data on temperature, humidity, soil parameters and other crop growth factors and uses this to classify crops into groups based on which grow best together on a given plot of land. It then provides ongoing recommendations and auction services to farmers on crop sales. The goal is to help farmers maximize yields by providing customized crop plans and real-time assistance based on machine learning predictions of weather and soil conditions.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Crop Recommendation System to Maximize Crop Yield using Machine Learning Tech...IRJET Journal
This document describes a crop recommendation system that uses machine learning techniques to maximize crop yields. The system collects soil data from testing labs and combines the data with crop information from experts. It then uses an ensemble model with majority voting to recommend crops for specific soil parameters. The ensemble uses support vector machine and artificial neural network learners to make recommendations with high accuracy. The goal is to help farmers choose crops best suited to their soil needs and increase productivity.
IRJET- Survey of Crop Recommendation SystemsIRJET Journal
This document summarizes and compares several papers on crop recommendation systems. It discusses papers that use techniques like artificial neural networks, ensemble models combining multiple algorithms like random trees and KNN, and algorithms like SVM. The document also compares the modules used in different systems like location detection, data analysis, similarity detection and recommendation generation. It concludes that using ensemble methods can improve accuracy over single algorithms and future work could integrate more factors like economic conditions and land area into recommendation systems.
IRJET- Crop Yield Prediction and Disease Detection using IoT ApproachIRJET Journal
This document proposes a system to predict crop yields and detect diseases using an IoT approach. The system would use sensors to monitor soil moisture levels, weather conditions, and other environmental factors. This data would be sent to a Raspberry Pi controller and stored in a database. Farmers could access this information through a mobile app to make informed decisions. The system would also automatically predict potential crop diseases based on changing conditions and provide prevention methods to farmers. This precision agriculture approach aims to help farmers save time and resources through better decision making supported by real-time sensor data analysis.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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2. Over 60% of Indians work in agriculture but farmers often grow the same crops without trying new varieties and apply fertilizers inconsistently, affecting yields and soil quality.
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Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models.
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This document provides a literature review and proposed methodology for an agricultural advisory system using data science techniques. It discusses several past studies that used machine learning algorithms like Naive Bayes, KNN, decision trees, and clustering for crop prediction and recommendations. The proposed system would collect agricultural data on parameters like rainfall, temperature and soil composition. It would preprocess, train and apply a supervised learning algorithm like Naive Bayes to provide priority-based crop recommendations to farmers based on location and year. The goal is to help farmers select suitable high-profit crops using data-driven techniques.
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This document describes a web application called Farm-Easy that aims to help farmers. It discusses:
1) Farm-Easy allows farmers and vendors to register and login. Vendors can update stock prices weekly and farmers can view predicted crop prices.
2) Related works explored e-agriculture platforms, agribusiness e-commerce systems, and different methods for predicting agricultural commodity prices.
3) Farm-Easy's methodology uses PHP and MySQL to develop separate vendor and farmer portals. Vendors update stock prices and farmers can view prices to make informed decisions. Naive Bayes is used to predict crop prices.
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This document proposes a smart agriculture system that uses machine learning algorithms like random forest to help farmers make better decisions. The system collects data on temperature, humidity, soil parameters and other crop growth factors and uses this to classify crops into groups based on which grow best together on a given plot of land. It then provides ongoing recommendations and auction services to farmers on crop sales. The goal is to help farmers maximize yields by providing customized crop plans and real-time assistance based on machine learning predictions of weather and soil conditions.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Crop Recommendation System to Maximize Crop Yield using Machine Learning Tech...IRJET Journal
This document describes a crop recommendation system that uses machine learning techniques to maximize crop yields. The system collects soil data from testing labs and combines the data with crop information from experts. It then uses an ensemble model with majority voting to recommend crops for specific soil parameters. The ensemble uses support vector machine and artificial neural network learners to make recommendations with high accuracy. The goal is to help farmers choose crops best suited to their soil needs and increase productivity.
IRJET- Survey of Crop Recommendation SystemsIRJET Journal
This document summarizes and compares several papers on crop recommendation systems. It discusses papers that use techniques like artificial neural networks, ensemble models combining multiple algorithms like random trees and KNN, and algorithms like SVM. The document also compares the modules used in different systems like location detection, data analysis, similarity detection and recommendation generation. It concludes that using ensemble methods can improve accuracy over single algorithms and future work could integrate more factors like economic conditions and land area into recommendation systems.
IRJET- Crop Yield Prediction and Disease Detection using IoT ApproachIRJET Journal
This document proposes a system to predict crop yields and detect diseases using an IoT approach. The system would use sensors to monitor soil moisture levels, weather conditions, and other environmental factors. This data would be sent to a Raspberry Pi controller and stored in a database. Farmers could access this information through a mobile app to make informed decisions. The system would also automatically predict potential crop diseases based on changing conditions and provide prevention methods to farmers. This precision agriculture approach aims to help farmers save time and resources through better decision making supported by real-time sensor data analysis.
Similar to Analyzing Cost of Crop Production and Forecasting the Price of a Crop (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Better Builder Magazine brings together premium product manufactures and leading builders to create better differentiated homes and buildings that use less energy, save water and reduce our impact on the environment. The magazine is published four times a year.
Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
This is an overview of my career in Aircraft Design and Structures, which I am still trying to post on LinkedIn. Includes my BAE Systems Structural Test roles/ my BAE Systems key design roles and my current work on academic projects.
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.