Agriculture plays a vital role in maintaining food security and contributing to the global economy. However, selecting the most appropriate crop for a specific region remains a significant challenge for farmers due to variations in soil nutrients, climatic conditions, and environmental factors. Incorrect crop selection can result in low productivity, inefficient resource utilization, and financial losses. With the growing availability of agricultural data and advancements in artificial intelligence, machine learning techniques have become effective tools for improving decision-making in agriculture. This study proposes an intelligent crop recommendation system that combines machine learning and deep learning models to assist farmers in selecting the most suitable crop based on soil and environmental conditions. The system analyses key agricultural parameters such as nitrogen (N), phosphorus (P), potassium (K), rainfall, soil pH, temperature, and humidity. These features are used to train predictive models capable of recommending the most appropriate crop for cultivation. Various 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 along with environmental attributes. Performance is assessed using evaluation metrics such as accuracy, precision, recall, and F1-score to identify the most effective model. Experimental results show that ensemble and deep learning models achieve high prediction accuracy in recommending suitable crops. The system also provides a user-friendly interface that enables farmers to input soil and environmental parameters and receive crop recommendations in real time. The proposed approach supports precision agriculture by enabling data-driven farming practices, improving crop yield, and assisting farmers in making informed decisions.
Harika et al. (Thu,) studied this question.