Titlebook: Deep Learning for Agricultural Visual Perception; Crop Pest and Diseas Rujing Wang,Lin Jiao,Kang Liu Book 2023 The Editor(s) (if applicable

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書目名稱Deep Learning for Agricultural Visual Perception影響因子(影響力)




書目名稱Deep Learning for Agricultural Visual Perception影響因子(影響力)學科排名




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書目名稱Deep Learning for Agricultural Visual Perception網(wǎng)絡公開度學科排名




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書目名稱Deep Learning for Agricultural Visual Perception被引頻次學科排名




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書目名稱Deep Learning for Agricultural Visual Perception年度引用學科排名




書目名稱Deep Learning for Agricultural Visual Perception讀者反饋




書目名稱Deep Learning for Agricultural Visual Perception讀者反饋學科排名

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Large-Scale Agricultural Pest and Disease Datasets,veloping advanced agricultural pest and disease recognition and detection algorithm. In general object detection community, there are various well-known datasets has been released, including the datasets of ImageNet Large Scale Visual Recognition Challenge [1], PASCAL VOC Challenges (VOC2007 and VOC

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有毛就脫毛

A CNN-Based Arbitrary-Oriented Wheat Disease Detection Method,ision technology, more accurate detection of crops in practical applications is a major trend in current smart agriculture, rather than just image classification in laboratory environments or simple environments. The ultimate goal of disease detection is to quantify the level of disease occurrence b

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Book 2023iseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with complex backgrounds as examples, a large-scale crop pest and disease dataset was constructed to provide necessary data support for the deep learning module. Various schemes for identifying and detecting large-sca

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le of agricultural pests and diseases.Integrates the artific.This monograph provides a detailed and systematic introduction to the application of deep learning technology in the intelligent monitoring of crop diseases and pests. Taking 24 types of crop pests, wheat aphids, and wheat diseases with co

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Fecundity

Judith Kearney,Lesley Wood,Richard Teareecision agriculture. Therefore, to promote the progress of crop protection, we constructed several large-scale pest datasets and disease dataset and released them, leading to the improvement of quality and yield of crop. Here, we have built two different crop pest dataset and one crop disease datasets.

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H. Dalke,A. Corso,G. Conduit,A. Riaze most samples with small scale and not friendly to small pest detection. In this chapter, we have comprehensively explored the small pest detection problem and addressed the above question to improve the recognition and detection.