QuickLeaf-LS 1.5: Software for grading of the leaf damage caused by leaf spot diseases using digital image processing

QuickLeaf-LS: Leaf Damage Assessment Software

Abstract

For mango (Mangifera Indica L.) fruit tree crops, a major tropical fruit of economic value, leaf spot diseases cause a significant decrease in yield and quality. The conventional procedure of assessing the degree of disease damage in leaf tissue employs manual grading, a time-consuming process that is subjective and prone to human error. Present study describe the software developed for quick, precise, and automated implementation of digital image processing techniques using MATLAB for leaf spot damage grading. The software can use both scanned and photographed images of plant leaves to measure disease severity. This software provided an integrated approach for image pre-processing, segmentation and quantification algorithms using feature extraction to yield objective grading. Software validation yielded good accuracy with R² =0.997, mean error rate =3.39%, mean accuracy rate=96.61%, RMSE= 1.260 and MAE= 0.619. Results showed that the system exhibits a very high accuracy in detecting and quantifying leaf spot lesions, which makes it a reliable tool for plant pathologists and crop scientists. This innovation has potential applications in disease monitoring, breeding programs, and precision agriculture.