Modeling Detecting Plant Diseases in Precision Agriculture: A NDVI Analysis for Early and Accurate Diagnosis

In precision agriculture, the accurate and timely plant disease identification is crucial. However, the lack of accuracy in current detection systems hampers reducing pesticide and fertilizer usage, causing significant productivity losses. The desired level of precision has not been achieved yet, hindering timely intervention and mitigation strategies. This research presents a novel approach that integrates a Lagrangian Gaussian Puff Dispersion Model (LGPTM) for assessing plant health, with Gaussian bell curve visualization, a tool for visualizing the distribution patterns of these indices in the field of precision agriculture. This integration ameliorates disease detection and monitoring in agricultural contexts, thereby improving disease management practices and enhancing crop health and productivity. The methodology leverages widely adopted libraries to process multispectral images and calculates vegetation index values based on the Normalized Difference Vegetation Index (NDVI). Additionally, the modeling approach employed modular programming. The code structure and execution encompass two main steps: the normalization of the Near-Infrared and Red bands of the multispectral images, and the construction of a three-dimensional Gaussian bell curve to visualize the distribution of vegetation indices using the meshgrid algorithmic technique. The results reveal a significant correlation between variations in the vegetation index and the vertical distribution of the Gaussian curve. Specifically, lower NDVI values indicate a diminished presence of vegetation or plant anomalies, resulting in an increase in the kurtosis of the Gaussian curve. To assess the effectiveness of the approach, Receiver Operating Characteristic analysis was employed, providing conclusive evidence regarding the reliability and performance of the implemented Python model.