APPLICATION OF ARTIFICIAL INTELLIGECE AND MACHINE LEARNING IN AGRICULTURE THROUGH MATHEMATICAL MODELING

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are playing a crucial role in transforming classic agriculture into advanced, technology-driven system. By using data analysis and mathematical modeling, these technologies assist informed and accurate decision-making in farming methods. This study describes major applications of AI and ML in agriculture, including plant disease identification, crop yield estimation, agricultural market price forecasting, intelligent irrigation management. Crop yield estimation is carried out using regression method that analyze past data such as soil characteristics, rainfall patterns and temperature variations, and to estimate future output. For disease detection, advanced image-processing techniques like Convolutional Neural Networks are used to leaf images to detect infections with large accuracy. Smart irrigation systems merge soil moisture sensors with machine learning technique to ensure optimal water distribution and efficient utilization of sources. In addition, time series forecasting models are utilized to predict crop prices by studying historical market trends.