A Systematic Review of Pomegranate Fruit Disease Detection and Classification Using Machine Learning and Deep Learning Techniques

In India Agriculture is the backbone of the economy and a source of employment. Agriculture contributes 20% to the GDP of India. There are many losses due to diseases that bring downcast efficiency and increase financial losses. Agricultural area needs to stand and progress from such problems to be highly gainful. This can be achieved by detecting diseases at appropriate stages of the fruit and plant lifespan. Suitable machine learning algorithms (linear and logistic regression, KNN, K-means, SVM, etc.) and deep learning algorithms (CNNs, LSTMs, RNNs, etc.) can be used with image processing techniques for identifying the various diseases in fruits and plants. Researchers developed various methods for the detection of diseases using the above techniques. This paper proposes to emphasize the review of research work in the agriculture field such as disease detection in pomegranate and checking algorithms, diseases, datasets, accuracy merits, demerits of each technique used. For other researchers working in the field of image processing for the detection and classification of leaf/fruit diseases, this review article will be crucial in understanding the state-of-the-art.