Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human-Computer Interaction for Industry 4.0

Abstract

When it comes to agricultural sciences, one of the most difficult challenges to solve is the detection of diseases. Agricultural specialists study a variety of sources to detect plant issues on a regular basis. Rarely can misinterpretations of diseased plants cause improper pesticide selection and subsequent agricultural disaster, although this does happen from time to time. In order to diagnose illnesses at an early stage, it is necessary to deploy automated disease detection systems. This is critical for farmers since it is both time-consuming and expensive. A sick leaf must be carefully segmented in order to be properly separate it from the rest of the leaves. Despite digital noise, a different background, a different shape, and a different brightness, it is tough to distinguish a sick photo. In order to increase the quality of apple leaf images for disease detection and classification, a new approach known as brightness preserving dynamic fuzzy histogram equalisation (BPDFHE) has been created. To determine the sweetness of an apple, examine the leaf and the texture of the fruit. In the next section, the performance of the proposed enhancement algorithm is compared to the performance of existing enhancement approaches. Existing segmentation algorithms are outperformed by our approach for segmenting the area of interest from ill leaves against a live background. It is during this phase that we analyse the Jaccard index, the Dice coefficient, and correctness. Comparing the proposed segmentation algorithm to current approaches, it proves to be a highly effective strategy that can more efficiently identify apple ill leaves from a live background with a 99.8 percent accuracy rate.