AgriBot: AI-Powered Precision Agriculture for Sustainable Crop Management
Mr. Vishal Miranda
Dept of Computer Science and Engg. Malnad College of Engineering, Hassan, India
Ms. Shreelakshmi M P
Dept of Computer Science and Engg. Malnad College of Engineering, Hassan, India
Mr. Srinath Gowda S M
Dept of Computer Science and Engg. Malnad College of Engineering, Hassan, India
Mr. Gokul H G
Dept of Computer Science and Engg. Malnad College of Engineering, Hassan, India.
Mrs.Nayana R
Assistant Professor
Dept of Computer Science and Engg.
Malnad College of Engineering, Hassan, India.
Abstract—This project focuses on developing an AI-driven chatbot to support farmers by providing instant responses to agricultural queries, offering crop recommendations based on soil characteristics, and identifying crop diseases through imagebased analysis. The chatbot will leverage machine learning techniques and natural language processing to ensure smooth and effective communication between farmers and the system. By utilizing soil data, it will deliver customized crop recommendations tailored to the unique conditions of the farmer’s land. Additionally, the image-based disease diagnosis module will enable farmers to upload photos of affected plants, which the system will analyze to deliver precise diagnoses and actionable treatment plans. The primary goal of this tool is to empower farmers with valuable insights, enhance their decision-making capabilities, and promote sustainable agricultural practices. By incorporating advanced machine learning algorithms and natural language processing, the chatbot will facilitate intuitive and effective interactions, enabling farmers to receive accurate and timely assistance. This innovative solution is intended to provide farmers with actionable advice , optimize agricultural decision-making, and support the adoption of sustainable farming practices. The goal of this project is to create an AI-powered chatbot designed to assist farmers by offering real-time solutions to agricultural challenges, recommending suitable crops based on soil properties, and diagnosing crop diseases through image recognition technology.
