標(biāo)題: Titlebook: Deep Learning for Agricultural Visual Perception; Crop Pest and Diseas Rujing Wang,Lin Jiao,Kang Liu Book 2023 The Editor(s) (if applicable [打印本頁] 作者: Consonant 時(shí)間: 2025-3-21 16:17
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(xué)科排名
書目名稱Deep Learning for Agricultural Visual Perception網(wǎng)絡(luò)公開度
書目名稱Deep Learning for Agricultural Visual Perception網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning for Agricultural Visual Perception被引頻次
書目名稱Deep Learning for Agricultural Visual Perception被引頻次學(xué)科排名
書目名稱Deep Learning for Agricultural Visual Perception年度引用
書目名稱Deep Learning for Agricultural Visual Perception年度引用學(xué)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(xué)科排名
作者: Foolproof 時(shí)間: 2025-3-21 22:59
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作者: 拍下盜公款 時(shí)間: 2025-3-22 02:26 作者: 有毛就脫毛 時(shí)間: 2025-3-22 05:57
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作者: Figate 時(shí)間: 2025-3-22 10:19
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作者: 客觀 時(shí)間: 2025-3-22 13:17
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作者: 客觀 時(shí)間: 2025-3-22 18:50 作者: flavonoids 時(shí)間: 2025-3-22 23:19 作者: Fecundity 時(shí)間: 2025-3-23 01:34
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.作者: Picks-Disease 時(shí)間: 2025-3-23 07:05
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.作者: pulmonary 時(shí)間: 2025-3-23 10:31
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].作者: 擁護(hù)者 時(shí)間: 2025-3-23 16:18
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.作者: irritation 時(shí)間: 2025-3-23 20:40 作者: decode 時(shí)間: 2025-3-23 23:23 作者: 故意 時(shí)間: 2025-3-24 04:17
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作者: 地名表 時(shí)間: 2025-3-24 07:47 作者: 芳香一點(diǎn) 時(shí)間: 2025-3-24 12:57
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 作者: SAGE 時(shí)間: 2025-3-24 18:48
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作者: 悲觀 時(shí)間: 2025-3-24 19:33
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作者: 不能逃避 時(shí)間: 2025-3-25 03:07
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作者: 健壯 時(shí)間: 2025-3-25 07:02
Marginalization of Young AdultsIn this chapter, we will mainly introduce basic concepts of neural networks to lay a good foundation for a better understanding of the content of the rest chapters.作者: 幾何學(xué)家 時(shí)間: 2025-3-25 10:02 作者: 旅行路線 時(shí)間: 2025-3-25 14:53
Rujing Wang,Lin Jiao,Kang LiuCombines a wide range of deep-learning-based modules for multi-classes pest and disease recognition.Covers multiple categories and large-scale of agricultural pests and diseases.Integrates the artific作者: 組裝 時(shí)間: 2025-3-25 19:45 作者: 施魔法 時(shí)間: 2025-3-25 21:18
https://doi.org/10.1007/978-981-99-4973-1Agricultural pest and disease; Convolutional neural network; Deep learning; Computer vision; Object dete作者: 投票 時(shí)間: 2025-3-26 02:20
978-981-99-4975-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: grieve 時(shí)間: 2025-3-26 07:02 作者: 護(hù)身符 時(shí)間: 2025-3-26 10:41 作者: accrete 時(shí)間: 2025-3-26 15:48 作者: Dysarthria 時(shí)間: 2025-3-26 20:32 作者: 周年紀(jì)念日 時(shí)間: 2025-3-26 22:29 作者: 反應(yīng) 時(shí)間: 2025-3-27 02:43
André Schaaffr?gung besitzt. Eine Skizzierung dieser beiden auf Vernetzungs- und Partizipationsstrukturen ausgerichteten Untersuchungsschwerpunkte erziehungswissenschaftlicher Forschung und damit verbundener forschungsmethodischer Herangehensweisen soll dies verdeutlichen. Sie mündet schlie?lich in ein Exzerpt d作者: 改良 時(shí)間: 2025-3-27 06:28 作者: 不理會(huì) 時(shí)間: 2025-3-27 11:41
Capillary Rollers and Boresme phase speed. The dynamical theory of the generation of parasitic capillaries has been developed by Longuet-Higgins (1963), Crapper (1970) and Ruvinsky et al. (1981, 1985, 1991). This so far takes into account only the first-order effects of viscous damping.作者: quiet-sleep 時(shí)間: 2025-3-27 15:28
erschienen sind. Der Verlag stellt mit diesem Archiv Quellen für die historische wie auch die disziplingeschichtliche Forschung zur Verfügung, die jeweils im historischen Kontext betrachtet werden müssen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ide作者: 別名 時(shí)間: 2025-3-27 19:08
