[33] ADVANCED INTELLIGENT AGRICULTURE SYSTEM FOR CROP YIELD AND GROWTH PREDICTION USING MACHINE LEARNING TECHNIQUES

How to Cite the Article: Ennam Govinda, Setti Sirisha, P. Raghava Kumari, M. Semal Sekhar, B. Ramesh Kumar & M. Ranganath Sarath (2026). Advanced Intelligent Agriculture System for Crop Yield and Growth Prediction Using Machine Learning Techniques. International Journal of Multidisciplinary Research & Reviews, 5(5),399-410. https://doi.org/10.56815/ijmrr.v5i5.2026.399-410

Abstract

Agriculture plays a vital role in the economic development of many countries, and improving crop productivity is essential for ensuring food security and sustainable farming. Traditional agricultural methods often rely on manual monitoring and experience-based decision-making, which may lead to inaccurate yield estimation and delayed disease detection. This paper presents an Advanced Intelligent Agriculture System for Crop Yield and Growth Prediction using Machine Learning and Deep Learning techniques. The proposed system integrates Machine Learning algorithms such as Linear Regression, Decision Tree Regression, and Random Forest Regression for crop yield prediction based on soil nutrients and environmental parameters. Additionally, a Deep Learning-based MobileNetV2 model is used for plant leaf disease detection through image classification. The system provides farmers with intelligent advisory recommendations, nutrient correction suggestions, and yield improvement analysis through an interactive Streamlit dashboard. Experimental results demonstrate that the Random Forest model achieves superior prediction accuracy compared to other models. The proposed system enhances agricultural productivity, supports precision farming, and enables data- driven decision-making for sustainable agriculture.