Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images

Lin, Shaodan; Li, Jiayi; Huang, Deyao; Cheng, Zuxin; Xiang, Lirong; Ye, Dapeng; Weng, Haiyong

doi:10.3390/plants12213675

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Lin et al [4] have made strides in early disease detection in rice using UAV imagery. Their approach involves a semi-supervised model that enhances the capability to identify diseases from aerial images.…”

Section: Agriculturementioning

confidence: 99%

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Enhancing unmanned aerial vehicle capabilities: integrating YOLO algorithms for diverse industrial applications

Guliutin,

Antamoshkin

2024

ITM Web Conf.

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The integration of UAVs with advanced deep learning algorithms, particularly the You Only Look Once models, has opened new horizons in various industries. This paper explores the transformative impact of YOLO-based systems across diverse sectors, including agriculture, forest fire detection, ecology, marine science, target detection, and UAV navigation. We delve into the specific applications of different YOLO models, ranging from YOLOv3 to the lightweight YOLOv8, highlighting their unique contributions to enhancing UAV functionalities. In agriculture, UAVs equipped with YOLO algorithms have revolutionized disease detection, crop monitoring, and weed management, contributing to sustainable farming practices. The application in forest fire management showcases the capability of these systems in real-time fire localization and analysis. In ecological and marine sciences, the use of YOLO models has significantly improved wildlife monitoring, environmental surveillance, and resource management. Target detection studies reveal the efficacy of YOLO models in processing complex UAV imagery for accurate and efficient object recognition. Moreover, advancements in UAV navigation, through YOLO-based visual landing recognition and operation in challenging environments, underscore the versatility and efficiency of these integrated systems. This comprehensive analysis demonstrates the profound impact of YOLO-based UAV technologies in various fields, underscoring their potential for future innovations and applications.

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Section: Agriculturementioning

confidence: 99%

“…Additionally, it predicts the class probabilities of the detected object. In agriculture [3][4][5][6][7][8], UAVs equipped with YOLO models have enhanced monitoring and disease detection. Studies have shown the algorithm's effectiveness in early disease detection in crops like rice, olives, maize, and apple trees.…”

Section: Introductionmentioning

confidence: 99%

Enhancing unmanned aerial vehicle capabilities: integrating YOLO algorithms for diverse industrial applications

Guliutin,

Antamoshkin

2024

ITM Web Conf.

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Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

Hongo,

Isono,

Sigit

et al. 2024

Agronomy

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In Indonesia, where the agricultural insurance system has been in full operation since 2016, a new damage assessment estimation formula for rice diseases was created through integrating the current damage assessment method and unmanned aerial vehicle (UAV) multispectral remote sensing data to improve the efficiency and precision of damage assessment work performed for the payments of insurance claims. The new method can quickly and efficiently output objective assessment results. In this study, UAV images and bacterial leaf blight (BLB) rice damage assessment data were acquired during the rainy and dry seasons of 2021 and 2022 in West Java, Indonesia, where serious BLB damage occurs every year. The six-level BLB score (0, 1, 3, 5, 7, and 9) and damage intensity calculated from the score were used as the BLB damage assessment data. The relationship between normalized UAV data, normalized difference vegetation index (NDVI), and BLB score showed significant correlations at the 1% level. The analysis of damage intensities and UAV data for paddy plots in all cropping seasons showed high correlation coefficients with the normalized red band, normalized near-infrared band, and NDVI, similar to the results of the BLB score analysis. However, for paddy plots with damage intensities of 70% or higher, the biased numbering of the BLB score data may have affected the evaluation results. Therefore, we conducted an analysis using an average of 1090 survey points for each BLB score and confirmed a strong relationship, with correlation coefficients exceeding 0.9 for the normalized red band, normalized near-infrared band, and NDVI. Through comparing the time required by the current assessment method with that required by the assessment method integrating UAV data, it was demonstrated that the evaluation time was reduced by more than 60% on average. We are able to propose a new assessment method for the Indonesian government to achieve complete objective enumeration.

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Early Detection of Rice Blast Using a Semi-Supervised Contrastive Unpaired Translation Iterative Network Based on UAV Images

Cited by 2 publications

References 27 publications

Enhancing unmanned aerial vehicle capabilities: integrating YOLO algorithms for diverse industrial applications

Enhancing unmanned aerial vehicle capabilities: integrating YOLO algorithms for diverse industrial applications

Efficient Damage Assessment of Rice Bacterial Leaf Blight Disease in Agricultural Insurance Using UAV Data

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