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

pulmonary

Introduction,crop pests, single control gradually transitions to diversity and integrated control, and the adoption of advanced intelligent technology for scientific pest control enhances the level of pest monitoring and can promote the benign development of agricultural economy [2].

擁護者

Large-Scale Agricultural Pest and Disease Datasets,ecision 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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故意

elligent science and technology, and other related fields in higher education institutions. It can also be used as a reference book for researchers in fields such as image processing technology, intelligent manufacturing, and high-tech applications..978-981-99-4975-5978-981-99-4973-1

地名表

芳香一點

A. Al Mahmud,Y. Limpens,J. B. Martensdevelopment of other fields. Due to the wide variety of crops, the types of pests and diseases also show a variety of trends, and some pests and diseases have serious damage, purely rely on pesticide control has a certain degree of difficulty [1]. In the early 1960s, international experts clarified

SAGE

Judith Kearney,Lesley Wood,Richard Teareveloping 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

悲觀

H. Dalke,A. Corso,G. Conduit,A. Riazill have some limitations, especially for small pests. We thoroughly explored why small-scale pests are difficult to detect and recognize in CNN. We found three reasons which lead to low detection accuracy of small pest. Firstly, the information which contributes to recognition multi-classes pests i

不能逃避

https://doi.org/10.1007/978-1-4471-2867-0ision 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