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ISSN 2095-5553 CN 32-1837/S
中华人民共和国农业农村部主管
农业农村部南京农业机械化研究所主办
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Vineyard scene obstacle detection based on edge devices
Cui Xuezhi, Feng Quan, Wang Shuzhi, Zhang Jianhua.
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142
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In order to achieve fast and accurate obstacle detection in vineyard field on unmanned agricultural machinery, four kinds of lightweight target detection neural networks, EfficientDetD0, YOLOV4TINY, YOLOV3TINY, and YOLOFASTEST, were trained with the independently built vineyard field scene dataset,and the training model eretransplanted to the edge device NVIDIA JETSON TX2. The accuracy of obstacle detection and the applicability of the four models on TX2 were evaluated. The results showed that the mAP of YOLOV3TINY, YOLOV4TINY, EfficientDetD0, and YOLOFASTEST were 0.648, 0.601, 0.598, and 0401, respectively. The experimental results on TX2 showed that the realtime video detection frame rates of the above network models were 34.24 frames, 24.75 frames, 2.34 frames, and 2.97 frames, respectively. Among the four target detection networks, YOLOV3TINY has the highest detection accuracy on the dataset and the fastest realtime detection speed, but it also consumes high hardware resources relatively. When considering the hardware resource consumption, YOLOV4TINY can better balance detection accuracy, running speed, and hardware resource consumption and achieve good results when running multiple tasks.
2021, 42 (9): 150-156.
doi:
10.13733/j.jcam.issn.2095-5553.2021.09.21
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Research on plant disease identification based on few-shot learning
Xiao Wei, Feng Quan, Zhang Jianhua, Yang Sen, Chen Baihong
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275
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In order to obtain high accuracy of plant disease classification with only a few training samples, a few-shot learning model was used as the disease classifier, and five kinds of shallow networks, Conv4, Conv6, ResNet10, ResNet18, and ResNet34, were used as feature extraction networks under the framework of three typical few-shot learning algorithms, including MatchingNet, ProtoNet, and RelationNet. Their performances were compared on the plant disease data set of PlantVillage. Under the condition of 5way、1shot, the average accuracies of MatchingNet, ProtoNet, and RelationNet were 72.29%, 72.43%, and 69.45%, respectively. ProtoNet+ResNet34 was the optimal combinational mode, and the accuracy reached 77.60%. Under the condition of 5way、5shot, the average accuracies of MatchingNet, ProtoNet, and RelationNet were 87.11%, 87.50%, and 82.92%, respectively. The accuracies were significantly improved compared to that of the 1shot condition. ProtoNet+ResNet34 was still the optimal one with an accuracy of 89.66%. The above test results show that by optimizing the combination of a few-shot learning framework and feature extraction network, the recognition model can achieve good effects for diseases with a small number of samples.
2021, 42 (11): 138-143.
doi:
10.13733/j.jcam.issn.20955553.2021.11.21