Sustainable Agriculture Practice using Machine Learning
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Abstract
The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2 - were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07%.
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References
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