Detection of rice plant disease using AdaBoostSVM classifier

Abstract

The research in the detection of plant diseases using plant images based on machine learning is widely increased in the field of agriculture. This could be done with the images of infected rice (Oryza sativa L.) plants. The changes in atmospheric condition cause changes in soil condition and in temperature. Both air temperature and soil temperature have distinct roles in crops, which can also lead to diseases in rice plants. In this paper, a prototype is developed for the detection of rice plant diseases like bacterial leaf blight, brown spot, and leaf smut. The proposed prototype is developed by undergoing experiments on image processing using machine learning algorithms. Several images of rice leaf which are infected by diseases had been captured and preprocessed using a median filtering technique. The important features are extracted by using Discrete Wavelet Transform (DWT) for the diseased part of the leaf images. Then the green parts of the leaf have been removed, so as to extract the diseased part. The features are extracted based on the attributes like color, shape, and texture. For multiclass classification process, Adaptive Boosting support vector machine (AdaBoostSVM) Classifier is used. The proposed prototype results in the accuracy of about 98.8% in detecting and classifying the rice leaf disease.

MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection

“…Incorporating the coordinate attention mechanism (CA) can provide more semantic information for the YOLOv7 object detection model, thereby improving its detection accuracy and efficiency. Specifically, the CA can assist the model in more accurately locating regions of interest and identifying properties, such as the shape and size of the detected objects [27]. In addition, introducing the CA can reduce unnecessary computational complexity, thereby improving the efficiency of the detection algorithm.…”

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

“…Without deliberate design, data-driven deep learning can automatically acquire global features. Kumar et al [10] created an adaptive Boosting SVM classifier for the identification of rice plant diseases, including bacterial leaf blight, brown spots, and leaf smut. It exhibited a sensitivity of up to 98.8 percent for recognising and diagnosing rice leaf diseases.…”

IMPROVED YOLOv8-BASED AUTOMATED DETECTION OF WHEAT LEAF DISEASES

“…Kumar et al used median filtering technique for preprocessing rice leaf diseases. The proposed prototype achieved an accuracy of approximately 98.8% in detecting and classifying rice leaf diseases (Kumar et al, 2022). However, with the popularity of large-scale cultivation and the accelerated spread of crop pathogens, traditional methods of manually extracting features for input into classifiers are gradually being eliminated (Thakur et al, 2022).…”