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Agri Ai: Intelligent Plant Disease Surveillance and Predictive Forecasting

Authors:

Dr. Jagini Naga Padmaja

Department of Artificial Intelligence and Machine Learning

Vardhaman College of Engineering

Hyderabad, India

nagapadmaja_14@vardhaman.org

Sai Asritha Thati

Department of Artificial Intelligence and Machine Learning       

Vardhaman College of Engineering Hyderabad, India

Saiasrithathati21aiml@vardhaman.org

Sai Sreeja Bandi

Department of Artificial Intelligence and Machine Learning

Vardhaman College of Engineering Hyderabad, India

bsaisreeja21aiml@student.vardhaman.org

Shalini Tejavath

Department of Artificial Intelligence and Machine Learning

Vardhaman College of Engineering Hyderabad, India

Tejavathshalini21aiml@student.vardhaman.org

Uma Devi R

Department of Artificial Intelligence and Machine Learning

Vardhaman College of Engineering Hyderabad, India

rumadevi21aiml@student.vardhaman.org

Dr. Gagandeep Arora

Department of Artificial Intelligence and Machine Learning

Vardhaman College of Engineering Hyderabad, India

Gagandeeparora250379@gmail.com

Abstract—Agriculture is the backbone of many economies, especially in India, as it is a considerable contributor to GDP and employment. Farmers struggle to predict crop yield due to the unpredictable nature of weather conditions, soil quality and other environmental factors. This paper implements machine learning (ML) technologies for yield prediction along with the remedies based on the temperature, rainfall, and soil composition. Different ML algorithms, such as Random Forest, Decision Tree, Neural Networks, and Regression models, are compared in terms of their accuracy. The proposed system mainly aims to help farmers with data-driven insights to optimize crop yield and enhance agricultural productivity.

Keywords: Machine learning; Random Forest; Yield prediction; Agricultural productivity.