Crop Disease Auto-localization and Classification

Abstract

AbstractPlant disease detection plays an important role in the agriculture field, as different diseases affecting the growth of plants is inevitable. If suitable measures are not taken in time with respect to this aspect, then it harms the crop, resulting in substantial decrease in the quality of yield produced. This is where AI can be quite useful. It reduces human interference in monitoring big farms and aids in the detection of symptoms of diseases in time to find a solution. Computer vision, machine learning and deep learning algorithms are being used to process this data. In this paper, digital image processing and deep learning models (different architectures and algorithms) were used for classification (type of microorganism and disease) with localization and detection of the diseases (pre-conditions/symptoms) present in the plant crops. An attempt was made to carry out image segmentation in a novel way, using the DICOM format. The GradCAM algorithm was used to perform a detailed analysis of various deep learning algorithms to validate the accuracy with which each algorithm classifies the diseases. Furthermore, bounding box regression was performed to locate the disease symptoms on each image. Lastly, the whole process was automated by hosting it on a web application for an easier user experience.KeywordsDeep learningClassificationLocalizationCropsImage processing