Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Abstract

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

Early Blight and Late Blight Disease Detection in Potato Using Efficientnetb0

“…Triangle Thresholding Segmentation was used to divide the dataset into primary and developed Brown Spots. This classification's overall accuracy was 99.20% (Upadhyay and Kumar, 2021).…”

A Hybrid ViT-CNN Model Premeditated for Rice Leaf Disease Identification

“…Upadhyay and Kumar [13] devised a straightforward, rapid, and efficient deep learning framework for the early detection of brown spot disease. This method integrates infection severity estimation via image processing techniques.…”

RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection

“…Likewise, the SVM classifier was fed with deep features obtained from Alex Network (AlexNet) CNN to classify rice diseases reaching an accuracy of 96.8%. Additionally, research articles [ 34 , 35 ] acquired deep features using a custom CNN to detect and classify growing diseases, achieving accuracies of 92.23% and 99.2%, respectively. The authors of the study [ 36 ] modified the Re-parameterization Visual Geometry Group (RepVGG) CNN model by adding an efficient channel attention mechanism to improve the performance in detecting rice diseases accomplishing an accuracy of 97.06%.…”