Crop Leaf Disease Prediction Using Machine Learning

Abstract

In India, crop area is largest in the world and produces major crops like wheat, pulses, fruits, rice and vegetables Despite of using modem taming techniques along with traditional, infectious plant diseases is major problem which can be caused by different viruses, fungus and bacteria. This mainly affects crop production as well as crop quality. It is very important to identify diseases at early stage Nowadays, automatic crop de detection has become a important research domain. It helps in detecting the symptoms of the disease when they are found on the e In this paper we will focus on finding the diseases in order to increase crop quality and production effectively. Here, we will focus on r diseases by observing leaves of plants at initial stage using machine learning. · In this paper, we designed a Deep Convolutional Neural Network based on LeNet to perform soybean leaf spot disease recognition and classification using affected areas of disease spots. The affected areas of disease spots were segmented from the leaves images using the Unsupervised fuzzy clustering algorithm. The proposed Deep Convolutional Neural Network model achieved a testing accuracy of 89.84%, and poor per class recognition results in 1378 images misclassified, and 1271 images correct classified. TheVGG16 achieved the best performance reaching a 93.54% success rate, and better per class recognition results in 1245 images misclassified, and 1404 images correct classified · In order to address the challenges related to the classification and recognition of soybean disease and healthy leaf identification, it is essential to have access to high-quality images. A meticulously curated dataset named “SoyNet” has been created to provide a clean and comprehensive dataset for research purposes. The dataset comprises over 9000 highquality soybean images, encompassing healthy and diseased leaves. These images have been captured from various angles and directly sourced from soybean agriculture fields; The soybean leaves images are organized into two sub-folders: SoyNet Raw Data and SoyNet Pre-processing Data.The SoyNet Pre-processing Data folder comprises resized images of 256∗256 pixels and the grayscale versions of disease and healthy images, following a similar organizational structure. We captured the images using the Nikon digital camera and the Motorola mobile phone camera, utilizing different angles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accurately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification