Hyperspectral Imaging and MATLAB Modelling Techniques for Next-Generation Plant Disease Forecasting

Ensuring global food security relies heavily on the capability to detect, quantify, and forecast plant diseases with high precision. With rapid advancements in computational technologies and precision agriculture, MATLAB has emerged as a powerful platform for modelling plant–pathogen interactions, analyzing hyperspectral data, and predicting yield losses due to major crop diseases. This study explores the transformative role of MATLAB in plant disease forecasting, emphasizing its value in mechanistic modelling, data-driven prediction, and early disease detection using multispectral and thermal imaging. By integrating machine learning algorithms, advanced image processing techniques, and climate-based disease progression models, MATLAB enables researchers to identify risk hotspots, simulate epidemic dynamics, and generate timely decision-support outputs. Experimental evaluation using soybean rust and wheat blast datasets demonstrates the platform’s ability to enhance predictive accuracy and diagnose stress factors long before visual symptoms appear. The research highlights MATLAB’s increasing relevance in precision agriculture and its potential to reduce chemical inputs, mitigate crop losses, and support sustainable food production systems.