Fruit Grading, Disease Detection, and an Image Processing Strategy

Abstract

It is difficult to maintain high quality in agriculture. Climate change has a wide range of effects on agriculture, including poorer crop yield and quality owing to drought, heat waves, flooding, and an increase in pests and  plant diseases. Such climate change makes it impossible to produce amazing items and meet human needs. Farmers would eventually be unable to profit from fruit growing. It is critical to be aware of climate change and educate farmers on how to deal with it. To boost the output and quality of pomegranate fruits, farmers need an automated system rather than a human one for fruit grading. Fruit grading by hand is unsuccessful because it takes more time to identify and grade diseases and necessitates the skills of a professional. It also does not yield appropriate results. Accurate fruit quality assessment and disease identification are vital tasks for farmers and researchers, as are pomegranate cultivation. Therefore, in this study, we developed a novel method for identifying  illnesses and classifying them according to colour, size, and disease. An image processing-based method for pomegranate fruit grading and illness detection is presented  in this study using Python. Machine learning methods like SVM or CNN are utilised to categorise the pomegranate fruit into different gradation groups based on the characteristics that were obtained.