標(biāo)題: Titlebook: Computer Vision and Machine Learning in Agriculture, Volume 3; Jagdish Chand Bansal,Mohammad Shorif Uddin Book 2023 The Editor(s) (if appl [打印本頁] 作者: 是英寸 時間: 2025-3-21 19:54
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3影響因子(影響力)
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3影響因子(影響力)學(xué)科排名
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書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3被引頻次
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3被引頻次學(xué)科排名
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書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3年度引用學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3讀者反饋
書目名稱Computer Vision and Machine Learning in Agriculture, Volume 3讀者反饋學(xué)科排名
作者: 心胸開闊 時間: 2025-3-21 21:01
Building a Custom Module Manuallyomic losses for farmers and reduced supplies for the sugar industry. In this research, we propose a solution for detecting three classes of sugarcane diseases using the YOLO algorithm. The YOLO version 8 model got a maximum accuracy of 96.67% after being trained and evaluated on a dataset of sugarca作者: 障礙 時間: 2025-3-22 03:06 作者: cravat 時間: 2025-3-22 08:23
Using Module Builder to Build Custom Modulesarliest to plan the food requirement of the rising population. Particularly in the field of computer vision, the deep learning approach has demonstrated superior performance over classical machine learning at identifying complicated structures in high-dimensional data. The proposed work focuses on c作者: CANE 時間: 2025-3-22 09:35 作者: sterilization 時間: 2025-3-22 14:30 作者: sterilization 時間: 2025-3-22 18:18 作者: Bravado 時間: 2025-3-22 22:34
Extending HTTP Sessions with Terracotta,n tasks, this research focused to identify models complexity, performance metrics and detection accuracy of deep learning-based model to detect crop diseases. Subsequently, this work implicitly depicts detection accuracy corresponds to hardware resources to ascertain trade-offs in relation to domain作者: CHASE 時間: 2025-3-23 03:07 作者: 矛盾心理 時間: 2025-3-23 09:26 作者: profligate 時間: 2025-3-23 09:58
The Definitive Guide to Terracottaote sensing, etc. The unique ability of a UAV to hover right above the target location and collect useful information makes it a suitable candidate for many such applications. UAV has been utilized in many key applications such as navigation and control, and visual-based detection, but autonomous de作者: 現(xiàn)代 時間: 2025-3-23 16:05 作者: PAD416 時間: 2025-3-23 20:57
Jagdish Chand Bansal,Mohammad Shorif UddinDescribes intelligent robots and drones.Discusses research outputs in precision agriculture.Presents applications of computer vision and machine learning (CV-ML) for better agricultural practices作者: 羞辱 時間: 2025-3-24 01:49 作者: crockery 時間: 2025-3-24 05:39
Computer Vision and Machine Learning in Agriculture, Volume 3978-981-99-3754-7Series ISSN 2524-7565 Series E-ISSN 2524-7573 作者: Innovative 時間: 2025-3-24 10:12
https://doi.org/10.1007/978-981-99-3754-7Agricultural Drones and Robots; Computer Vision, Machine Learning, and Deep Learning Tools; Precision作者: CERE 時間: 2025-3-24 13:10
978-981-99-3756-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: OTHER 時間: 2025-3-24 15:16 作者: Optimum 時間: 2025-3-24 21:29
,Computer Vision and?Machine Learning in?Agriculture: An Introduction,The agriculture sector is the backbone of most of the country’s economy.作者: Intractable 時間: 2025-3-25 00:11 作者: 愚笨 時間: 2025-3-25 03:19
2524-7565 hine learning (CV-ML) for better agricultural practices.This book is as an extension of the previous two volumes on “Computer Vision and Machine Learning in Agriculture”. This volume 3 discusses solutions to the problems of agricultural production by rendering advanced machine learning including dee作者: 不幸的人 時間: 2025-3-25 08:37 作者: 無脊椎 時間: 2025-3-25 12:46 作者: 沉積物 時間: 2025-3-25 19:52
Book 2023, disease detection, plant recognition, production yield, product quality, defect assessment, and overall automation through robots and drones. The topics covered in the current volume, along with the previous volumes, are comprehensive literature for both beginners and experienced in this domain..作者: chondromalacia 時間: 2025-3-25 21:39 作者: transient-pain 時間: 2025-3-26 02:03 作者: 尖 時間: 2025-3-26 07:47 作者: 懶洋洋 時間: 2025-3-26 08:41
,RGB to?Multispectral Remap: A?Cost-Effective Novel Approach to?Recognize and?Segment Plant Disease,ltispectral images reconstructed from RGB images. This image preserves the radiant spectral energy at individual wavelength ranging from 380 to 730?nm. In this study, we are reporting the efficiency of such method to segment plant diseases that achieved 81.3% dice score.作者: saphenous-vein 時間: 2025-3-26 14:00
Building a Custom Module Manuallya, pomegranate, and strawberry. We also validated our dataset using some of the existing pre-trained deep learning models including Inception V3, VGG 16, VGG 19, ResNet50, and MobileNet V2. We got the best accuracy of 96.04% for VGG 16. This will certainly accelerate automation in the agri-industry.作者: 抵消 時間: 2025-3-26 20:25 作者: 卷發(fā) 時間: 2025-3-27 00:21 作者: 連鎖,連串 時間: 2025-3-27 02:53 作者: 拍下盜公款 時間: 2025-3-27 09:15 作者: 鋪子 時間: 2025-3-27 11:50 作者: 蒼白 時間: 2025-3-27 17:03
,A New Methodology to?Detect Plant Disease Using Reprojected Multispectral Images from?RGB Colour Sp importance, feasibility, and applicability of the proposed method to identify plant diseases with affordable limits. The research found that the proposed model able to improve 4.35% detection accuracy compare to RGB colour-based images using identical deep learning-based detection model. To do so, 作者: Obscure 時間: 2025-3-27 20:51
,Analysis of?the?Performance of?YOLO Models for?Tomato Plant Diseases Identification,ction scores on detection accuracy, precision, recall and F-1 score. However, YOLO-5 tiny performs better in terms of detection time but comprises detection accuracy. In this study, a publicly available data set name . has been used.作者: legacy 時間: 2025-3-27 21:59
,Strawberries Maturity Level Detection Using Convolutional Neural Network (CNN) and?Ensemble Method,oposed based on SqueezeNet, GoogleNet, and VGG-16. Based on the considered performance matrices, SqueezeNet is recommended as the most effective model among all the classifiers and networks for detecting and classifying the maturity levels of strawberries.作者: 威脅你 時間: 2025-3-28 05:22 作者: 戰(zhàn)役 時間: 2025-3-28 09:17
Leveraging Computer Vision for Precision Viticulture, automation, posing new challenges and objectives that have not yet been explored. This work intends to deliver a complete guide of the current status of computer vision in viticulture, covering all management practices, such as pruning, binding, shoot thinning, weeding, spraying, leaf thinning, top作者: 闖入 時間: 2025-3-28 12:51
2524-7565 nd overall automation through robots and drones. The topics covered in the current volume, along with the previous volumes, are comprehensive literature for both beginners and experienced in this domain..978-981-99-3756-1978-981-99-3754-7Series ISSN 2524-7565 Series E-ISSN 2524-7573 作者: Outmoded 時間: 2025-3-28 14:50 作者: ascend 時間: 2025-3-28 21:40 作者: 逃避責(zé)任 時間: 2025-3-28 23:04 作者: 松果 時間: 2025-3-29 04:02
Grid Computing Using Terracotta, importance, feasibility, and applicability of the proposed method to identify plant diseases with affordable limits. The research found that the proposed model able to improve 4.35% detection accuracy compare to RGB colour-based images using identical deep learning-based detection model. To do so, 作者: Bureaucracy 時間: 2025-3-29 09:18 作者: photopsia 時間: 2025-3-29 12:34
https://doi.org/10.1007/978-1-4302-0639-2oposed based on SqueezeNet, GoogleNet, and VGG-16. Based on the considered performance matrices, SqueezeNet is recommended as the most effective model among all the classifiers and networks for detecting and classifying the maturity levels of strawberries.作者: 使混合 時間: 2025-3-29 16:46
The Definitive Guide to Terracottabust computation, convolutional neural network (CNN) model for target detection and recognition, while proximate probing is performed using a PID-based algorithm, ensuring UAV hover right above the target. The proposed framework has been developed in five successive steps by adopting Lawson’s sense,作者: CYT 時間: 2025-3-29 22:47 作者: Conclave 時間: 2025-3-30 02:11
Deep Learning Modeling for Gourd Species Recognition Using VGG-16,y cases, which leads urban people to get confused to recognize the vegetables properly. One instance of the most confusing vegetables is the gourd vegetables of the Cucurbitaceae family such as sponge gourd, ridge gourd, and snake gourd. Computer vision and deep learning can help in this regard thro作者: 得意人 時間: 2025-3-30 08:04 作者: 涂掉 時間: 2025-3-30 08:46 作者: 演講 時間: 2025-3-30 14:12 作者: Hangar 時間: 2025-3-30 19:28
,Advances in?Deep Learning-Based Technologies in?Rice Crop Management,us on biotic stress diagnosis, yield prediction, and other factors affecting agricultural productivity. This chapter gives insights into DL techniques for managing rice crop cultivation. In addition, several applications, such as rice yield estimation, phenological analysis, disease detection, pest 作者: endocardium 時間: 2025-3-30 22:41
AI-Based Agriculture Recommendation System for Farmers,to use ancient techniques. New crop production techniques proliferated as a result of the introduction of improved seed varieties. However, without the use of better methods, the yield from the agricultural sector remains inadequate. Common challenges faced by Indian farmers include (i) inability to作者: 關(guān)節(jié)炎 時間: 2025-3-31 02:43
