2022
DOI: 10.3389/fpls.2022.972286
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Abstract: Accurate and timely surveys of rice diseases and pests are important to control them and prevent the reduction of rice yields. The current manual survey method of rice diseases and pests is time-consuming, laborious, highly subjective and difficult to trace historical data. To address these issues, we developed an intelligent monitoring system for detecting and identifying the disease and pest lesions on the rice canopy. The system mainly includes a network camera, an intelligent detection model of diseases an… Show more

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Cited by 9 publications
(5 citation statements)
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“…YOLO-DPD was compared to YOLOv4 and YOLOv4 + RFA to detect and identify three diseases and pests on the rice canopy and the mAP of YOLO-DPD reached 92.24%. Comparing YOLOv4 and YOLOv4 + RFA, the mAP of YOLO-DPD was increased by 8.06% and 1.62%, respectively [ 26 ]. On the other hand, we encountered different studies that demonstrated that YOLO performs better than SSD in object detection.…”
Section: Experimental Results and Analysesmentioning
confidence: 99%
See 1 more Smart Citation
“…YOLO-DPD was compared to YOLOv4 and YOLOv4 + RFA to detect and identify three diseases and pests on the rice canopy and the mAP of YOLO-DPD reached 92.24%. Comparing YOLOv4 and YOLOv4 + RFA, the mAP of YOLO-DPD was increased by 8.06% and 1.62%, respectively [ 26 ]. On the other hand, we encountered different studies that demonstrated that YOLO performs better than SSD in object detection.…”
Section: Experimental Results and Analysesmentioning
confidence: 99%
“…Zhang et al introduced an improved YOLOv5 network to detect unopened cotton bolls in the field precisely, which can effectively assist farmers to take effective approaches in time and reduce crop losses and increase production [ 25 ]. Li et al proposed a YOLO-DPD model that combines the residual feature augmentation and attention mechanism modules to detect and identify three diseases and pests on the rice canopy [ 26 ]. Guo et al presented an automatic monitoring scheme to detect vegetable insect pests using a monitoring device and a YOLO-SIP detector, and the average accuracy was 84.22% [ 27 ].…”
Section: Introductionmentioning
confidence: 99%
“…For example, a recent study by Wang et al (2022) trained a CNN to identify three common insect pests caught on sticky traps located in apple orchards on the basis of their colour and shape. The YOLO-Diseases and Pests Detection (YOLO-DPD) model achieved a recognition accuracy of over 90% in detecting lesions of three species of diseases and pests on rice canopy, demonstrating its potential as a CNN tool for pest management (Li et al, 2022). Another study tested five different CNNs to recognize multiple insect pests in images of cotton plants (Johari et al, 2023).…”
Section: Image-based Pest Identificationmentioning
confidence: 99%
“…The management of these diseases and pests requires a significant amount of manual input for agricultural plant protection operations, resulting in a sharp rise in labor costs (Yongliang et al, 2019;Brown et al, 2022). Therefore, intelligent plant protection information systems such as rice canopy pest monitoring systems (Li et al, 2022), field pest monitoring and forecasting systems (Liu et al, 2022), meteorological monitoring systems, and crop disease real-time monitoring and early warning systems have been widely used. The visualization and digitization of pest information improve the efficiency of pest forecasting and reduces the amount of work for plant protection staff at the grassroots level.…”
Section: Introductionmentioning
confidence: 99%