• Vikas Biradar

Agriculture plays an important role in the nourishment of economies and food security. Farmers, however, face challenges like crop choice, disease identification, and nutrient optimization, which have a significant influence on productivity. The rapid growth of artificial intelligence (AI) and machine learning (ML) has presented opportunities to solve such challenges in an efficient manner. This research paper presents AgroAI, an AI-driven agriculture support system to facilitate farmers in making the right decisions. AgroAI combines machine learning models with an ease-of-use web application to provide end-to-end solutions for crop recommendation, plant disease identification, and fertilizer recommendation. The system relies on a Random Forest model to recommend the most appropriate crops based on soil parameters, environmental conditions, and weather forecast. It also employs a ResNet9 model, a convolutional neural network, to detect plant diseases from leaf images with a high accuracy level. The fertilizer recommendation system identifies soil nutrient content and recommends corrective measures to achieve maximum plant health. The suggested system is deployed as a local server using Flask to ensure easy accessibility, performance, and real-time data processing. The modular nature of the system allows the provision to integrate additional models and features, ensuring flexibility in varied agricultural settings. System testing demonstrates its effectiveness in enhancing agricultural productivity, crop loss reduction, and enhancing sustainable farming. By delivering accurate suggestions and insights to farmers, AgroAI seeks to enhance the modernization of agriculture, promoting a data-driven approach to farming.

Smart Agriculture, AI in precision farming, Crop and disease prediction models, ResNet9 model, Random Forest classifier, Precision farming

"AgroAI-Agriculture Support System Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.g912-g917, May-2025, Available :http://www.jetir.org/papers/JETIR2505795.pdf

"AgroAI-Agriculture Support System Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppg912-g917, May-2025, Available at : http://www.jetir.org/papers/JETIR2505795.pdf