Smart Crop Recommendation: A Hybrid AI System Integrating Machine Learning and Deep Learning for Precision Agriculture

Authors- Mr.A.Janardana Rao, R.Usha, K.Bala Venkata Adithya, P.Anil Kumar, Ch.E Naga Sai Priya, S.Satya Kumar

Abstract-Recent advancements in agriculture have led to the development of crop recommendation systems, offering a new approach to optimizing crop yields and the efficient use of resources. In this study, we introduced an enhanced “Crop Recommendation System” designed to assist farmers in selecting suitable crops based on various soil and environmental factors. Our system leverages machine learning (ML) and deep learning (DL) models to enhance agricultural productivity and crop yields. The dataset used for training and evaluation was sourced from Kaggle’s repository, featuring key parameters such as nitrogen (N), phosphorus (P), potassium (K), rainfall, pH, temperature, humidity, and crop labels corresponding to 22 different crop types. The study incorporates several ML and DL algorithms, including Decision Trees, Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, Artificial Neural Networks, Deep Neural Networks, and Temporal Convolutional Networks. According to performance metrics, Random Forest and TCN delivered the highest accuracy, achieving 99.2% and 99.9%, respectively. Other models also performed impressively, with accuracy ranging between 93.8% and 98.7%. Although Support Vector Machine (SVM) showed slightly lower performance, with 93.4% accuracy, it still yielded satisfactory results. The project also explores parameter tuning for XGBoost and TCN, with TCN outperforming XGBoost after optimization. The findings of this research indicate that both machine learning and deep learning models are highly effective in crop recommendation systems, wisth TCN providing accurate and efficient recommendations. Furthermore, this study aids precision agriculture by offering a web-based interface for farmers, helping them select crops based on environmental and soil conditions.

DOI: /10.61463/ijset.vol.13.issue2.258