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 AIdriven
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.