AI-Driven Predictive Modeling for Crop Disease Detection

Abstract

The efficient and safe production of crops is critical to addressing global food security challenges, which are exacerbated by rapid population growth. Among the numerous factors affecting agricultural productivity, plant diseases play a significant role in hindering crop yields. The accurate and timely detection of these diseases has become a pivotal area of research. Geographic Information Systems (GIS)-supported farming information systems have emerged as a powerful alternative to traditional farming methods, enabling more precise decision-making by overcoming the limitations of human-based approximations. Data mining (DM) techniques have been widely adopted for crop disease detection, leveraging large volumes of agronomic data to uncover hidden patterns and predictive insights that humans might overlook. Despite extensive research, some emerging technologies still show relatively lower success rates in diagnosing certain crop diseases. To address these challenges, Artificial Intelligence (AI) and Artificial Intelligence Recognition (AIR) models have been proposed as promising solutions. By utilizing hyperspectral imagery generated through AI-based pattern recognition, these models can detect specific crop diseases, such as head blight, with an estimated 70% accuracy. This approach represents a significant advancement in agricultural disease detection and forecasting, contributing to more efficient and sustainable crop management practices.