Lime Diseases Detection and Classification Using Spectroscopy and Computer Vision

Abstract

In the agricultural industry, plant diseases and pests pose the greatest risks. Lime is rich 10 source of vitamin C which works as an immunity booster in human body. Because of the late and manually diseases detection in lime causes a vast loss in crop production worldwide. The most common diseases are found in limes are lime canker, lemon scab, brown rot, sooty mould and Armillaria. In this paper we used imaging and non-imaging (spectral based sensing) methods with the combination of machine learning technique to detect the lime canker and sooty mould diseases. Image acquirement, pre-processing, segmentation and classification are all steps in the imaging methodology, which is then followed by feature extraction. In non-imaging methodology a multispectral sensor (Spectrometer) is used with 400 nm to 1000 nm wavelength to detect the diseases. training set and test set ratio is fixed for both techniques are 75% and 25% respectively. When it comes to identifying and classifying lime disease, spectroscopy has a 99% efficiency rating compared to imaging methodology's 96%.