IntelliFarm: A Unified AI-Driven Decision Support System for Crop Planning, Disease Detection, and Market Price Forecasting

Authors: Siddesh Bhaskar Demse, Nilesh Sham Sonawane, Hitesh Sanjaysing Girase, Dipak Shantaram Lohar

DOI: https://doi.org/10.37082/IJIRMPS.v14.i3.233149

Short DOI: https://doi.org/hb6xnm

Country: India

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Abstract: Modern agriculture faces challenges such as climate variability, crop diseases, and fluctuating market prices, making traditional experience-based farming less effective. This paper presents IntelliFarm+, an Artificial Intelligence (AI)-based decision support system designed to assist farmers in crop recommendation, crop disease detection, and crop price forecasting. The crop recommendation module utilizes a Random Forest classifier to suggest suitable crops based on soil and climatic conditions. Crop disease detection is performed using DenseNet-based Convolutional Neural Networks (CNNs) to identify diseases from leaf images. For market analysis, Long Short-Term Memory (LSTM) networks are employed to forecast crop prices using historical and recent market data. The system is implemented as a scalable web-based platform with a modular architecture, enabling real-time interaction and decision support. Experimental results demonstrate strong performance, with crop recommendation achieving 99.55% accuracy, disease detection reaching 95.05% for grape leaves and 90.67% for sugarcane leaves, and crop price forecasting attaining 80.4% accuracy with a 7.0% mean absolute percentage error. These findings indicate that IntelliFarm+ can effectively support precision agriculture by enabling informed, data-driven farming decisions.

Keywords: Smart Agriculture, Artificial Intelligence, Crop Recommendation, Crop Disease Detection, Crop Price Forecasting, Deep Learning, LSTM, Precision Agriculture.

Paper Id: 233149

Published On: 2026-06-08

Published In: Volume 14, Issue 3, May-June 2026

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