Plant Disease Detection App Using Convolutional Neural Network

Abstract

Crop diseases pose significant challenges to the agricultural sector, resulting in substantial production losses and economic setbacks. The efficacy of conventional disease diagnosis techniques, such optical examination of plant leaves, is constrained. It is imperative to improve agricultural disease detection, monitoring, and prediction in order to address this problem. This work offers a mobile-based system that uses the plant-DOC Dataset to automate the diagnosis of plant leaf diseases. The system is powered by machine learning (ML) and computer vision. Deep learning algorithms, in particular Convolutional Neural Networks (CNNs), are used in the suggested model. CNNs are excellent at identifying diseases in plants and crops. Finding the plant's sickness is the aim of this application. Additionally, image processing can be used to detect the disease type, the appropriate pesticide, and to alert farmers so that prompt action can be performed. The whole process involves gathering plant images, preprocessing them by resizing, normalizing, and augmenting, segmenting if necessary, extracting features, selecting, and training a CNN model, evaluating its performance, and using it to detect plant diseases. In this paper, we have successfully developed both the frontend and backend components for integration into our proposed model, accompanied by corresponding screenshots showcased within the document. Key Words: plant leaf diseases, agriculture, mobile app, computer vision, machine learning, deep learning, Convolutional Neural Networks.