標(biāo)題: Titlebook: Computer Vision and Machine Learning in Agriculture, Volume 2; Mohammad Shorif Uddin,Jagdish Chand Bansal Book 2022 The Editor(s) (if appl [打印本頁(yè)] 作者: 實(shí)體 時(shí)間: 2025-3-21 18:22
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2影響因子(影響力)
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2被引頻次
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2被引頻次學(xué)科排名
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2年度引用
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2年度引用學(xué)科排名
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2讀者反饋
書(shū)目名稱(chēng)Computer Vision and Machine Learning in Agriculture, Volume 2讀者反饋學(xué)科排名
作者: promote 時(shí)間: 2025-3-21 20:33 作者: Virtues 時(shí)間: 2025-3-22 00:30
Advanced Component Architecture,d coriander) and two different orchards (loquat and peach). The developed system outperformed its competitors with 91.3% mean average precision (mAP) and a processing time of 0.235?s. Thus, the proposed framework provided an excellent potential to be deployed on autonomous systems (UAVs, robots, etc作者: Bridle 時(shí)間: 2025-3-22 05:35 作者: abject 時(shí)間: 2025-3-22 12:18 作者: 救護(hù)車(chē) 時(shí)間: 2025-3-22 14:10
Customizing Forms and Core Templates,essment. It can provide qualitative and quantitative data under single analysis. This chapter ensures a critical review on spectroscopic and imaging techniques combined chemo metric analysis, which achieves better accuracy of 99% for food quality analysis, role of machine learning and deep learning 作者: 救護(hù)車(chē) 時(shí)間: 2025-3-22 20:05
Using JSPs and Servlets in Stellent,detects and classifies input plant leaf data as healthy or diseased using SVM and kNN classifier, where SVM gives better accuracy of 93.67%. The obtained results indicate that the proposed methodology outperforms the other algorithms in obtaining good classification accuracy.作者: Minatory 時(shí)間: 2025-3-22 22:38 作者: 粗糙 時(shí)間: 2025-3-23 05:12 作者: Lasting 時(shí)間: 2025-3-23 06:42
Customizing Forms and Core Templates, CNN-SVM classifier is shown to be a fast, extremely efficient method for classifying specific imaging features into desired disease classes, as well as giving preferable results over the plain CNN and other classifiers, such as the support vector machine (SVM) for large datasets. Finally, the exper作者: breadth 時(shí)間: 2025-3-23 11:23 作者: locus-ceruleus 時(shí)間: 2025-3-23 16:41 作者: 同謀 時(shí)間: 2025-3-23 19:51
Real-Life Agricultural Data Retrieval for Large-Scale Annotation Flow Optimization,More advanced architectures such as transformers have also not been applied to this data before. This chapter presents a solution to speed up annotation time by providing annotators semantically similar images to their target image. An image retrieval task is conducted to map crop images to a single作者: Basal-Ganglia 時(shí)間: 2025-3-23 22:45 作者: fodlder 時(shí)間: 2025-3-24 03:22
Agri-Food Products Quality Assessment Methods,essment. It can provide qualitative and quantitative data under single analysis. This chapter ensures a critical review on spectroscopic and imaging techniques combined chemo metric analysis, which achieves better accuracy of 99% for food quality analysis, role of machine learning and deep learning 作者: indignant 時(shí)間: 2025-3-24 07:12
,ESMO-based Plant Leaf Disease Identification: A?Machine Learning Approach,detects and classifies input plant leaf data as healthy or diseased using SVM and kNN classifier, where SVM gives better accuracy of 93.67%. The obtained results indicate that the proposed methodology outperforms the other algorithms in obtaining good classification accuracy.作者: 大喘氣 時(shí)間: 2025-3-24 12:54
Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning,isease classes. The dataset is improved and expanded using various data augmentation techniques on the training images. Experimental analysis on the Plant Pathology 2021-FGVC8 dataset shows that our proposed model achieves remarkable precision, recall, and .1-score of 0.9743, 0.9541, and 0.9625, res作者: thwart 時(shí)間: 2025-3-24 15:12 作者: 小歌劇 時(shí)間: 2025-3-24 21:06
Early Stage Prediction of Plant Leaf Diseases Using Deep Learning Models, CNN-SVM classifier is shown to be a fast, extremely efficient method for classifying specific imaging features into desired disease classes, as well as giving preferable results over the plain CNN and other classifiers, such as the support vector machine (SVM) for large datasets. Finally, the exper作者: rectocele 時(shí)間: 2025-3-25 00:37
2524-7565 . The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies..978-981-16-9993-1978-981-16-9991-7Series ISSN 2524-7565 Series E-ISSN 2524-7573 作者: 違抗 時(shí)間: 2025-3-25 06:15
2524-7565 ped with the touch of CV-ML.Focuses on optimized disease rec.This book is as an extension of previous book “Computer Vision and Machine Learning in Agriculture” for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting produc作者: 謙虛的人 時(shí)間: 2025-3-25 10:10
Advanced Component Architecture,ilar colored objects in the background scenario. Our experimental results revealed that the ResNet50 model efficiently recognized two major maturity stages of coconuts with the top-1 accuracy of 98.32% and top-5 accuracy of 99.85% for the test size 0.10 and top-1 accuracy of 98.53% and top-5 accuracy of 100% for the test size 0.30.作者: Feature 時(shí)間: 2025-3-25 14:59
Using JSPs and Servlets in Stellent,nV3, MobileNet, and Xception are investigated to find their respective efficacy. Extensive experiments are performed using the developed data set to recognize 11 medicinal plants from their leaf images. MobileNet deep CNN architecture confirms the optimum performance based on four evaluation metrics derived from the confusion matrix.作者: 撕裂皮肉 時(shí)間: 2025-3-25 17:09
Advanced Component Architecture, black rot, buttoning, and white rust using a dataset containing around 2500 images. Among the investigated different CNN models, InceptionV3 produced 93.93% test accuracy, which is much superior compared to other similar experiments in recent times.作者: insurrection 時(shí)間: 2025-3-25 22:43 作者: 最高峰 時(shí)間: 2025-3-26 00:26 作者: 跟隨 時(shí)間: 2025-3-26 05:16 作者: 賞心悅目 時(shí)間: 2025-3-26 09:33
An Intelligent System for Crop Disease Identification and Dispersion Forecasting in Sri Lanka,ispersion patterns are visualized in GIS-based heat maps. The remedy recommendations are made based on the expertise of the necessary agricultural authorities. The system has been implemented and tested for the detection of fungal diseases found in potato, tomato, and bean plants with an accuracy ranging from 90 to 94%.作者: motor-unit 時(shí)間: 2025-3-26 13:58
Site Settings and Best Practices,eness. This chapter describes the automated harvesting of some common fruits and vegetables, such as tomatoes, apples, litchi, sweet peppers, and kiwifruit with the help of robots. The limitations of the existing harvesting robots are pointed out, and suggestions are made for new research directions for further advancements.作者: 謊言 時(shí)間: 2025-3-26 17:13
Using JSPs and Servlets in Stellent,t of efficiency (CE). The results revealed that the SVM model performed slightly better than fuzzy model based on RMSE and CE. The SVM and fuzzy models outperformed the MLR model which involves restrictive assumptions such as linearity, normality and homoscedasticity.作者: 字的誤用 時(shí)間: 2025-3-26 22:43 作者: LAY 時(shí)間: 2025-3-27 01:34 作者: 裂口 時(shí)間: 2025-3-27 08:34 作者: 微塵 時(shí)間: 2025-3-27 13:21 作者: octogenarian 時(shí)間: 2025-3-27 16:05
Book 2022interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvestin作者: ungainly 時(shí)間: 2025-3-27 18:56 作者: 某人 時(shí)間: 2025-3-27 23:54
Drone-Based Weed Detection Architectures Using Deep Learning Algorithms and Real-Time Analytics, previously used for military purposes are now being equipped with sophisticated sensorial devices for data acquisition, and algorithms are being developed for autonomous flights. In parallel to the upgrades on drones, other fields such as real-time analytics and deep learning algorithms are also ex作者: 大笑 時(shí)間: 2025-3-28 05:43
A Deep Learning-Based Detection System of Multi-class Crops and Orchards Using a UAV,illion people. However, obtaining this figure is difficult due to weather and natural disasters. Modernizing this field’s technologies can help achieve the desired outcome. Precision agriculture is a type of ICT-based technology that has significantly increased global productivity. Since their intro作者: 小畫(huà)像 時(shí)間: 2025-3-28 08:24 作者: 圣歌 時(shí)間: 2025-3-28 13:52 作者: antedate 時(shí)間: 2025-3-28 16:33 作者: follicular-unit 時(shí)間: 2025-3-28 19:05 作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-29 02:59
Agri-Food Products Quality Assessment Methods,Various forms of malnutrition, misuse of additives, pathogenic microorganisms and toxins affect directly or indirectly the health of individuals around the globe. The heart of this problem was raised because the farmers concentrated on maximizing the yields with large quantities and earning profit w作者: 機(jī)警 時(shí)間: 2025-3-29 05:09 作者: 熄滅 時(shí)間: 2025-3-29 08:10 作者: 防銹 時(shí)間: 2025-3-29 13:05 作者: Assemble 時(shí)間: 2025-3-29 19:33 作者: 雀斑 時(shí)間: 2025-3-29 22:18
Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning,rs that directly affect fruit harvest and apple agriculture, just as they do in every other commercial farming. Unfortunately, apple orchards are under incessant peril from umpteen fungal, bacterial, viral pathogens, and insects over the growing season. The present research work aims to detect and i作者: insincerity 時(shí)間: 2025-3-30 01:23 作者: 運(yùn)氣 時(shí)間: 2025-3-30 04:50 作者: Trochlea 時(shí)間: 2025-3-30 10:33
Computer Vision and Machine Learning in Agriculture, Volume 2978-981-16-9991-7Series ISSN 2524-7565 Series E-ISSN 2524-7573 作者: GILD 時(shí)間: 2025-3-30 13:06 作者: 人類(lèi)的發(fā)源 時(shí)間: 2025-3-30 18:53 作者: Deject 時(shí)間: 2025-3-30 22:48
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.Focuses on optimized disease rec
