Crop Recommendation System for Farmers Using Convolutional Neural Network

Abstract: Agriculture plays an important role in food security and the economic development of many countries. Selecting the most suitable crop for a particular field is a major challenge for farmers because crop growth depends on soil properties, nutrients, temperature, humidity, rainfall, pH, and climatic conditions. Traditional crop selection mainly depends on farmers experience and local knowledge, which may not always provide accurate recommendations under changing environmental conditions. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) provide effective solutions for data-driven agricultural decision making. This paper presents a review of crop recommendation systems with particular emphasis on Convolutional Neural Networks (CNNs). CNNs can automatically learn useful patterns from soil images, remote-sensing images, and other agricultural data, while CNN- based hybrid models can also be combined with environmental and soil parameters. Recent studies have explored Random Forest, XGBoost, Support Vector Machine, LSTM, CNN, attention-based networks, Transformer models, and hybrid deep-learning architectures for crop recommendation. This review examines research published from 2020 to 2026, summarizes their datasets, preprocessing methods, algorithms, and major findings, and identifies research gaps related to dataset availability, regional generalization, explainability, multimodal data integration, and real-time deployment. A CNN-based crop recommendation system can potentially provide farmers with localized and intelligent recommendations while reducing inappropriate crop selection, improving productivity, and supporting sustainable farming.

Keywords: Crop Recommendation, Convolutional Neural Network, CNN, Deep Learning, Precision Agriculture, Smart Farming, Soil Classification, Machine Learning, Farmers, Agriculture.