Mango Skin Disease Detection Techniques Based on Machine Learning Techniques: A Review

The mango, commonly called the “king of fruits,” is India’s most significant commercial fruit crop and a member of the Anacardiaceous family. Due to its extensive production, it is vulnerable to numerous variables affecting the quantity and quality of mangoes. Diseases in mangoes are brought on by microbes such as algae, parasites, bacteria, and fungi at every step of the plant’s development, from seedling through fruit consumption. The illnesses might appear as sooty mould, canker, spots, wilt, stem haemorrhage, anthracnose, blotch, scab, necrosis, mildew, dieback, rot, and deformity. Plant diseases significantly reduce production and result in financial loss. Crop examiners can recognize such diseases using various methods, which are relatively expensive and time-consuming; the solutions suggested are sometimes inaccurate and occasionally biased. Thus, this research introduces a review of the mango disease detection technique proposed to identify the research gaps and develop a novel framework. For this, various mango skin disease detection methods are gathered from various online sources and categorized those articles into deep learning techniques, fuzzy-based techniques, digital X-ray imaging techniques and machine learning techniques. Followed by the analysis of the research articles is devised based on the year of publication, the database utilized for processing the method, tools utilized for simulation, and assessment measures. Finally, the research gaps are identified, and the future scope is elaborated for developing a novel framework with enhanced detection accuracy.