2022
DOI: 10.3390/agronomy12092096
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Classification Method of Significant Rice Pests Based on Deep Learning

Abstract: Rice pests are one of the main factors affecting rice yield. The accurate identification of pests facilitates timely preventive measures to avoid economic losses. Some existing open source datasets related to rice pest identification mostly include only a small number of samples, or suffer from inter-class and intra-class variance and data imbalance challenges, which limit the application of deep learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we first r… Show more

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Cited by 16 publications
(9 citation statements)
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“…Furthermore, because Detectron2 incorporates several widely employed deep learning models for object detection and instance segmentation, it possesses the potential for future compatibility with a broader range of agricultural and industrial production scenarios. These scenarios may include tasks like recognizing plant fructifications and identifying crop pests, extending its applicability beyond the sole measurement of rapeseed pod phenotype omics data [ 62 , 63 , 64 , 65 , 66 ]. By combining machine vision, we also determined the length, width, and two-dimensional image area of the rapeseed pods in the image using a single coin as a reference.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, because Detectron2 incorporates several widely employed deep learning models for object detection and instance segmentation, it possesses the potential for future compatibility with a broader range of agricultural and industrial production scenarios. These scenarios may include tasks like recognizing plant fructifications and identifying crop pests, extending its applicability beyond the sole measurement of rapeseed pod phenotype omics data [ 62 , 63 , 64 , 65 , 66 ]. By combining machine vision, we also determined the length, width, and two-dimensional image area of the rapeseed pods in the image using a single coin as a reference.…”
Section: Discussionmentioning
confidence: 99%
“…The comparative pest detection results of the FFADL-ARPDC technique are demonstrated in Table 5 and Fig. 15 [25,26]. The FFADL-ARPDC approach exemplifies extensive progress in the domain of agricultural image analysis, especially for the classification of rice pests from plant images.…”
Section: Experimental Validationmentioning
confidence: 91%
“…The authors combined VGG19 and RPN models and obtained 89.22% insect detection and classification accuracy. Pattnaik et al [34] used the transfer learning (DenseNet169) technique to classify pests in tomato plants. The collection includes 859 photos of tomato pests divided into 10 classifications, and 88.83% accuracy was attained by DenseNet-169.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The collection includes 859 photos of tomato pests divided into 10 classifications, and 88.83% accuracy was attained by DenseNet-169. The IP102 dataset was reorganized by Li et al [34] and given the name IP_RicePests. VGGNet, ResNet, and MobileNet networks were used to train the model.…”
Section: Literature Reviewmentioning
confidence: 99%