Abstract: This project focuses on building a smart and sustainable agricultural system that assists farmers in reducing the overuse of chemical fertilizers and pesticides. Excessive chemical application has long-term negative effects on soil fertility, crop yield, and the surrounding ecosystem. To address these issues, the system utilizes Artificial Intelligence (AI) techniques to provide personalized recommendations for suitable crops, fertilizers, and pesticides based on the specific soil condition and plant health data. The application allows farmers to register using their basic details and input soil data either manually (by entering nutrient values such as Nitrogen, Phosphorus, Potassium, pH level, and moisture) or by uploading images. Based on this information, the AI model predicts the most suitable crop for cultivation. Additionally, the system features a disease detection module where farmers can upload images of infected leaves. Using machine learning and image processing techniques, the system diagnoses the disease and suggests appropriate remedies, including the type and quantity of fertilizers or pesticides required.
Keywords: Artificial Intelligence, Sustainable Agriculture, Crop Recommendation, Soil Analysis, Plant Disease Detection, Machine Learning, Fertilizer Suggestion, Pesticide Management, Smart Farming, Image Classification, Precision Agriculture, IoT Integration, Environmental Protection, Farmer Support, Agricultural Technology, Leaf Image Analysis, Soil Nutrient Detection, Decision Support System, Cloud-Based Farming, Multilingual Interface, Mobile Agriculture App, Digital Farming, Agricultural Data Analytics, Smart Irrigation, Deep Learning, Computer Vision, Crop Yield Optimization, Remote Sensing, Farm Management System, Agricultural Sustainability
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DOI:
10.17148/IJARCCE.2025.14524
