2022
DOI: 10.1007/978-981-16-6624-7_19
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Deep Learning for Real-Time Diagnosis of Pest and Diseases on Crops

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Cited by 8 publications
(6 citation statements)
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“…Li et al [36] developed a real-time plant disease and pest identification system on video using faster R-CNN as an object detection framework. Gambhir et al [37] designed a CNN-based dynamic android and web UI for agricultural pest and disease detection. The findings suggested that the proposed method could identify previously unseen rice diseases on video.…”
Section: Related Workmentioning
confidence: 99%
“…Li et al [36] developed a real-time plant disease and pest identification system on video using faster R-CNN as an object detection framework. Gambhir et al [37] designed a CNN-based dynamic android and web UI for agricultural pest and disease detection. The findings suggested that the proposed method could identify previously unseen rice diseases on video.…”
Section: Related Workmentioning
confidence: 99%
“…In addition, Koklu et al developed a deep feature based on CNN-SVM to classify 5 species of grapevine leaves, in which a classification accuracy of 97.60% was achieved [27]. Gambhir et al developed a CNN-based interactive android and web interface for the diagnosis of pests and diseases on crops [28]. Li et al developed a real-time plant disease and pest recognition system on video through faster R-CNN as an object detection framework [22].…”
Section: Related Workmentioning
confidence: 99%
“…Sabanci et al proposed a convolutional recurrent hybrid network combining AlexNet and BiLSTM for the detection of pest-damaged wheat [ 12 ]. Gambhir et al developed a CNN-based interactive network robot to diagnose pests and diseases on crops [ 13 ]. Sun et al proposed a multi-scale feature fusion instance detection method based on SSD, which improved on SSD to detect maize leaf blight in complex backgrounds [ 14 ].…”
Section: Introductionmentioning
confidence: 99%