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A staple food in more than a hundred nations worldwide is rice (Oryza sativa). The cultivation of rice is vital to global economic growth. However, the main issue facing the agricultural industry is rice leaf disease. The quality and quantity of the crops have declined, and this is the main cause. As farmers in any country do not have much knowledg...
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... the prevalence of rice leaf diseases. According to [2], rice disease damage can considerably reduce productivity. They are primarily caused by bacteria, viruses, or fungi. Applying computer vision technology to the management of diseases is the simplest and frequently most cost-effective technique. The global spread of the rice disease is seen in Fig. 2. Bacterial leaf blight, Brown Spot, Leaf Blast, Leaf Scald, Leaf Tungro, Leaf Ufra, Narrow Brown Leaf Spot, and Sheath Blight are a few of the diseases that can affect rice leaves. Fig. 3 represents the four rice leaf diseases we discovered throughout our research: bacterial leaf blight, brown spot, leaf blast, and sheath blight. In ...
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... discovered 0.807 mAP (Mean Average Precision) using the precision-recall curve, as depicted in Fig. 19. Using the training and verification sets, the network was trained. After 80 training batches, the detection frame loss, detection object loss, and classification loss value curves for the training and verification sets were established shown in Fig. ...
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Citations
... Extensive research on rice bacterial blight detection using deep learning has been conducted. However, prior studies by Haque et al. (2022); Jia et al. (2023); Kumar et al. (2023), and Prasomphan (2023) did not fully consider the complexities of field conditions and the diverse angles at which diseases appear under the UAV viewpoint. This study addresses these specific needs. ...
Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.
... The YOLOv5 model was trained and evaluated on a dataset of approximately 1500 photos. The use of a large dataset during training resulted in high accuracy [29]. ...
Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network’s ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network’s feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method’s strong performance and feasibility for rice disease classification in real-life scenarios.
