Intelligent Crop Recommendation System Using Machine Learning And Deep Learning For Precision Agriculture

Agriculture plays a crucial role in ensuring food security and supporting the global economy. However, selecting the most suitable crop for a particular region remains a major challenge for many farmers due to variations in soil nutrients, climate conditions, and environmental factors. Incorrect crop selection can lead to reduced productivity, inefficient use of resources, and financial losses. With the increasing availability of agricultural data and advances in artificial intelligence, machine learning techniques have emerged as powerful tools for improving agricultural decision-making.This study presents an intelligent crop recommendation system that integrates machine learning and deep learning models to assist farmers in selecting the most suitable crop based on soil and environmental conditions. The proposed system analyses important agricultural parameters such as nitrogen (N), phosphorus (P), potassium (K), rainfall, soil pH, temperature, and humidity. These features are used to train predictive models that can recommend the optimal crop for cultivation.Several machine learning and deep learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Temporal Convolutional Networks (TCN), are implemented and evaluated. The models are trained using a publicly available agricultural dataset containing multiple crop types and environmental attributes. Performance evaluation is conducted using metrics such as accuracy, precision, recall, and F1-score to determine the most effective model.Experimental results demonstrate that ensemble and deep learning models achieve high prediction accuracy in recommending suitable crops. The system also includes a user-friendly interface that allows farmers to input soil and environmental parameters and receive crop recommendations in real time.The proposed approach contributes to the development of precision agriculture systems by supporting data-driven farming practices, improving crop productivity, and helping farmers make more informed agricultural decisions.