Optimizing Agriculture Using Machine Learning Techniques
Abstract
This project focuses on developing an integrated system for agricultural optimization, including soil prediction, crop recommendation, plant disease detection, fertilizer suggestion, and crop yield prediction. The goal is to assist farmers in making informed decisions to improve agricultural productivity and sustainability through data-driven insights. The system leverages advanced machine learning and deep learning techniques to provide comprehensive support across various aspects of farming. The system begins by analyzing soil images to classify the soil type using a Convolutional Neural Network (CNN) model. Based on the identified soil type, it recommends suitable crops using Random Forest and XGBoost algorithms. The system also includes a plant disease detection module using a CNN model based on the MobileNet architecture. Farmers can upload leaf images, and the model identifies common diseases like blight, rust, and leaf spot. Early detection allows timely intervention, reducing crop loss and improving produce quality. For each disease, the system provides management strategies to prevent further spread. Fertilizer recommendations are made using Random Forest and XGBoost based on plant disease. Finally, the project employs LSTM to predict crop yield by considering various location, name of the area and soil parameters. This integrated approach assists farmers in making informed decisions for optimal crop selection, disease management, and maximizing agricultural productivity. Overall, the project demonstrates the potential of AI-driven solutions to address complex challenges in agriculture and contribute to global food security efforts. The project highlights AI's potential in tackling agricultural challenges, supporting sustainable farming practices, optimizing resources, and boosting productivity, for global food security.
Keywords – Soil analysis, Crop yield prediction, plant disease identification, CNN, Random Forest, XGBoost, and LSTM.
