Mango Plant Disease Detection System Using Hybrid BBHE and CNN Approach

Abstract

Detection of plant diseases plays a crucial role in taking disease control measures to increase the quality and quantity of crops produced. Plant disease automation is beneficial because it eliminates surveillance work at significant farms. As plants are a food source, diagnosing leaf conditions early and accurately is essential. This work involves a detailed learning approach that automates leaf disease detection in mango plant species. This paper presents a detection system using Brightness Preserving Bi-Histogram Equalization (BBHE) and Convolutional Neural Network (CNN). The photographs of mango leaves were first flattened, then resized and translated to their threshold value, followed by feature extraction. CNN and BBHE have extensively been used for pattern recognition. The test images of affected leaves were subsequently uploaded to the system and then matched to the ailments being trained. Training data and test data were cross-validated to balance over-adjustment and under-adjustment problems. The proposed method correctly detects the mango leaves disease at the early stage with 99.21% maximum accuracy.