A Step-by-Step Procedure to Identify the Diseases in Pomegranate Fruit and Leaves at Early Stage Using Convolution Neural Networks

Pomegranate is one of the fruits which is commercially sold and is mostly cultivated in semiarid and arid regions in India. Pomegranates are one of the highly consumed fruits as they are rich in carbohydrates, calcium, vitamin-c, iron, and citric acid. And, these fruits are also used as the medicinal agent for the diseases like diabetes, cancer, hypertension, heart, and kidney-related diseases. Though there are many advantages for pomegranate, but the major issue which is affecting the production of this fruit is the disease on fruit and leaves due to climate changes and pest. Identifying this disease on fruit and leaf in early stages will improve the production and also beneficial to farmers. The most common diseases for the pomegranate fruit will be Bacterial Blight, Anthracnose, Fruit Spot, Fruit Borer, and Fusarium Wilt, and coming to the leaves, the diseases are diverse bacterial blight symptoms observed on pomegranate leaves. In this proposed research, the concept of deep neural networks with image processing will be applied in order to identify the diseases at early stages. To imply this system, the data will be collected from the field images starting from leaf stage to the production stage of the fruit starting from 25 to 60 days. There is no limitation on the number of images which are going to be captured, as many images as possible will be captured as it will be given as an input to the machine learning model. As machine learning mechanism says that the size of the input is big, the accuracy levels will be always better. Once it is loaded, same will be applied with various ML algorithms in order to find out the diseases which are affecting fruits and leaves. The proposed research is majorly to identify the diseases of pomegranate fruit and leaves at early stages.