International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agriculture is essential to the economies of countries like India, but it faces challenges such as changing climate, poor soil nutrients, and outbreaks of plant diseases. To tackle these problems, this study introduces a Smart Crop and Fertilizer Recommendation System with Plant Disease Identification that uses machine learning and deep learning techniques. The web-based system gives real-time suggestions for suitable crops and fertilizers based on data about soil nutrients, temperature, humidity, pH, and rainfall. We trained and evaluated seven machine learning models: Decision Tree, Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, XGBoost, and K-Nearest Neighbours (KNN) for crop recommendations. The Random Forest model achieved the highest accuracy and was chosen as the best option. Additionally, the system generates fertilizer recommendations to fix soil nutrient shortages and boost crop yield. The system also includes plant disease identification using a Convolutional Neural Network (CNN), which analyses leaf images to classify diseases accurately. This allows for early detection and timely response. This integrated solution helps users make informed decisions, supports sustainable farming, and improves productivity and food security.

Keywords: CNN, crop recommendation, machine learning, plant disease identification, random forest

Downloads: | DOI: 10.17148/IJARCCE.2026.15204