標(biāo)題: Titlebook: Computer Vision and Machine Learning in Agriculture; Mohammad Shorif Uddin,Jagdish Chand Bansal Book 2021 The Editor(s) (if applicable) an [打印本頁] 作者: frustrate 時間: 2025-3-21 17:15
書目名稱Computer Vision and Machine Learning in Agriculture影響因子(影響力)
書目名稱Computer Vision and Machine Learning in Agriculture影響因子(影響力)學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture網(wǎng)絡(luò)公開度
書目名稱Computer Vision and Machine Learning in Agriculture網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture被引頻次
書目名稱Computer Vision and Machine Learning in Agriculture被引頻次學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture年度引用
書目名稱Computer Vision and Machine Learning in Agriculture年度引用學(xué)科排名
書目名稱Computer Vision and Machine Learning in Agriculture讀者反饋
書目名稱Computer Vision and Machine Learning in Agriculture讀者反饋學(xué)科排名
作者: effrontery 時間: 2025-3-21 23:15
,Robots and Drones in Agriculture—A Survey,s is resulting in famine, which causes a dreadful recession in the economy. To bridge this gap, automation in agriculture has been assembled with diverse robotics technologies by replacing traditional farming processes to improve agricultural efficiency. Robotics in agriculture generally represents 作者: Immobilize 時間: 2025-3-22 02:25 作者: amplitude 時間: 2025-3-22 06:23
A Multi-Plant Disease Diagnosis Method Using Convolutional Neural Network,oaches to recognize plant diseases are often temporal, challenging, and time-consuming. Therefore, computerized recognition of plant diseases is highly desired in the field of agricultural automation. Due to the recent improvement of computer vision, identifying diseases using leaf images of a parti作者: Diluge 時間: 2025-3-22 10:18
A Deep Learning-Based Approach for Potato Disease Classification,ing strategies. A dataset is generated using 1574 images of various diseases. This dataset is expanded to 7870 images through the data augmentation technique by utilizing scaling and rotation. Experimentation is performed by dividing the data into training and testing categories at a ratio of 8:2. T作者: 反省 時間: 2025-3-22 15:52 作者: 反省 時間: 2025-3-22 20:20 作者: 窗簾等 時間: 2025-3-22 23:08
An Efficient Bag-of-Features for Diseased Plant Identification, techniques have been proven to be quite useful. However, the diseased plant identification is still a challenging task due to the disparity in the leaf images. To alleviate the same, this chapter proposes a new bag-of-features-based diseased plant identification method. In the proposed method, the 作者: 不能根除 時間: 2025-3-23 01:42 作者: 白楊 時間: 2025-3-23 08:56 作者: GEON 時間: 2025-3-23 11:03 作者: 包庇 時間: 2025-3-23 17:49 作者: FLORA 時間: 2025-3-23 21:27 作者: 性行為放縱者 時間: 2025-3-24 02:02
,Spring Web Flow’s Architecture,sNet-101, VGG-16, and MobileNet have been applied, and it is found that ResNet-101 gives the highest accuracy of 96.93% for beneficial and 95.07% for non-beneficial pest detection and identification compared to other networks.作者: somnambulism 時間: 2025-3-24 04:20 作者: 天文臺 時間: 2025-3-24 10:33
The Definitive Guide to Spring Web Flowthe state-of-the-art techniques and shows ways for future work. This work is expected to be very much useful for the new and old researchers in the area of machine vision-based fruit and vegetable disease recognition.作者: TAP 時間: 2025-3-24 12:11 作者: 曲解 時間: 2025-3-24 16:41 作者: 協(xié)定 時間: 2025-3-24 20:18 作者: 燈絲 時間: 2025-3-25 01:15 作者: 信任 時間: 2025-3-25 05:28 作者: hazard 時間: 2025-3-25 10:13 作者: 含糊其辭 時間: 2025-3-25 15:28 作者: Exclaim 時間: 2025-3-25 19:27
2524-7565 ped with the touch of CV-ML.Presents deep learning tools and.This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agri作者: Noctambulant 時間: 2025-3-25 21:15
Mohammad Shorif Uddin,Jagdish Chand BansalDiscusses applications of computer vision and machine learning (CV-ML) for better agricultural practices.Describes intelligent robots developed with the touch of CV-ML.Presents deep learning tools and作者: 口味 時間: 2025-3-26 04:07 作者: 難聽的聲音 時間: 2025-3-26 04:45
The Definitive Guide to Spring Web Flowty. The demand for efficient as well as reliable food production techniques is rapidly increasing day by day. Computer vision tagged with machine learning approaches grabbed considerable attention for research to meet this demand through analyzing and understanding the input images from humans, robo作者: 巧思 時間: 2025-3-26 08:51
,Spring Web Flow’s Architecture,s is resulting in famine, which causes a dreadful recession in the economy. To bridge this gap, automation in agriculture has been assembled with diverse robotics technologies by replacing traditional farming processes to improve agricultural efficiency. Robotics in agriculture generally represents 作者: Original 時間: 2025-3-26 15:48
,Spring Web Flow’s Architecture,creased productivity. In this chapter, a region-based deep convolutional neural network (Faster R-CNN) where a deep CNN is connected with a region proposal network (RPN) is used to perform the detection and identification paddy pests from the images. The main focus of this chapter is to find out not作者: 狂怒 時間: 2025-3-26 20:25 作者: 使成波狀 時間: 2025-3-26 22:47
,Spring Web Flow’s Architecture,ing strategies. A dataset is generated using 1574 images of various diseases. This dataset is expanded to 7870 images through the data augmentation technique by utilizing scaling and rotation. Experimentation is performed by dividing the data into training and testing categories at a ratio of 8:2. T作者: 憲法沒有 時間: 2025-3-27 04:02
https://doi.org/10.1007/978-1-4302-1625-4segmentation, it is important to determine and find an optimal technique for a particular context. For an automated machine vision-based fruit disease recognition context, image segmentation plays a very important role for extracting features from the location and size of defective areas. In this re作者: violate 時間: 2025-3-27 08:04
The Definitive Guide to Spring Web Flowen made using different computer vision techniques to address different problems of agriculture. The machine vision-based diagnosis of fruits and vegetables is a notable problem domain in this regard. This problem domain has beckoned the computer vision and machine learning researchers to contribute作者: 縮影 時間: 2025-3-27 09:41 作者: 無目標(biāo) 時間: 2025-3-27 15:14 作者: COMA 時間: 2025-3-27 21:26
978-981-33-6426-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 連接 時間: 2025-3-28 01:12 作者: 看法等 時間: 2025-3-28 02:57
,Spring Web Flow’s Architecture,uation, we have collected data from various online sources that included leaf images of six plants, including tomato, potato, rice, corn, grape, and apple. In our investigation, we implement numerous popular convolutional neural network (CNN) architectures. The experimental results validate that the作者: 發(fā)誓放棄 時間: 2025-3-28 08:50
Detection of Rotten Fruits and Vegetables Using Deep Learning,作者: 可用 時間: 2025-3-28 11:15 作者: 刺耳的聲音 時間: 2025-3-28 16:24
Computer Vision and Machine Learning in Agriculture作者: craven 時間: 2025-3-28 19:43
