International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Agriculture plays a vital role in the economic growth of many developing countries, yet farmers often face challenges related to improper crop selection, inefficient fertilizer usage, and delayed identification of plant diseases. Recent advancements in artificial intelligence offer promising solutions to address these issues through data-driven decision support systems. This paper presents a Smart Farming AI system that integrates machine learning and deep learning techniques to assist farmers in making informed agricultural decisions. The proposed system provides crop recommendations based on soil nutrient levels and environmental parameters, fertilizer suggestions using nutrient deficiency analysis, and plant disease detection through image-based analysis. The system is implemented as a web-based application using the Flask framework, ensuring accessibility and ease of use. Additionally, the application supports camera-based image capture and multilingual interaction to improve usability for farmers with limited technical knowledge. Experimental evaluation demonstrates that the system can generate accurate and explainable recommendations in a controlled environment. The proposed solution serves as an effective educational and decision-support platform for demonstrating the application of artificial intelligence in agriculture.

Keywords: Smart Farming, Machine Learning, Deep Learning, Crop Recommendation, Fertilizer Recommendation, Plant Disease Detection, Artificial Intelligence, Precision Agriculture.

Downloads: | DOI: 10.17148/IJARCCE.2026.151136