標題: Titlebook: Digital Ecosystem for Innovation in Agriculture; Sanjay Chaudhary,Chandrashekhar M. Biradar,Mehul S Book 2023 The Editor(s) (if applicable [打印本頁] 作者: Inspection 時間: 2025-3-21 19:16
書目名稱Digital Ecosystem for Innovation in Agriculture影響因子(影響力)
書目名稱Digital Ecosystem for Innovation in Agriculture影響因子(影響力)學科排名
書目名稱Digital Ecosystem for Innovation in Agriculture網絡公開度
書目名稱Digital Ecosystem for Innovation in Agriculture網絡公開度學科排名
書目名稱Digital Ecosystem for Innovation in Agriculture被引頻次
書目名稱Digital Ecosystem for Innovation in Agriculture被引頻次學科排名
書目名稱Digital Ecosystem for Innovation in Agriculture年度引用
書目名稱Digital Ecosystem for Innovation in Agriculture年度引用學科排名
書目名稱Digital Ecosystem for Innovation in Agriculture讀者反饋
書目名稱Digital Ecosystem for Innovation in Agriculture讀者反饋學科排名
作者: genesis 時間: 2025-3-21 20:30
2197-6503 with digital agriculture.Brings together a group of top schThis book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview o作者: acclimate 時間: 2025-3-22 04:07
Theresia Ratih Dewi Saputri,Seok-Won Leers or modelling with a focus on a specific aspect of climate change and agriculture. This chapter summarizes such tools and their application in accelerating transdisciplinary collaboration for more sustainable and climate-resilient agriculture.作者: Conjuction 時間: 2025-3-22 06:52 作者: HEAVY 時間: 2025-3-22 10:50
Gitosree Khan,Sabnam Sengupta,Anirban Sarkarporal information. They are subsequently fed into a fully connected network (FCN) to predict the yield. The chapter demonstrates that adding information about vegetation indices improves yield prediction.作者: DEFER 時間: 2025-3-22 16:14
A Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Chars or modelling with a focus on a specific aspect of climate change and agriculture. This chapter summarizes such tools and their application in accelerating transdisciplinary collaboration for more sustainable and climate-resilient agriculture.作者: DEFER 時間: 2025-3-22 19:07
An Algorithmic Framework for Fusing Images from Satellites, Unmanned Aerial Vehicles (UAV), and Farm exploiting their complementarity. In this chapter, we present an algorithmic framework that exploits the synergies among the three data sources to construct a high-dimensional farm map. We present an outline of how this framework can help in the construction of farm map in the context of crop monitoring.作者: 蜈蚣 時間: 2025-3-23 00:11 作者: cuticle 時間: 2025-3-23 04:26 作者: 減少 時間: 2025-3-23 08:25
Colin M. Werner,Daniel M. Berrytive of size, location, and economic background. This structure could direct agricultural organizations in their knowledge management initiatives by exploring various ecosystem components for better operation and usage.作者: 易于出錯 時間: 2025-3-23 12:46
Machine Learning and Deep Learning in Crop Management—A Reviewes faced while applying the ML and DL algorithms to different crop management activities. Moreover, the available agriculture data sources, data preprocessing techniques, ML algorithms, and DL models employed by researchers and the metrics used for measuring the performance of models are also discussed.作者: 礦石 時間: 2025-3-23 13:51
A Theoretical Framework of Agricultural Knowledge Management Process in the Indian Agriculture Contetive of size, location, and economic background. This structure could direct agricultural organizations in their knowledge management initiatives by exploring various ecosystem components for better operation and usage.作者: refine 時間: 2025-3-23 21:37
A Brief Review of Tools to Promote Transdisciplinary Collaboration for Addressing Climate Change Chagricultural efficiencies and greening the energy sector by making room for sustainable renewable bioenergy crops. Efforts for climate change mitigation and adaptation with a focus on agriculture must come from transdisciplinary collaboration, which is often not easy and require one to come out of on作者: 終止 時間: 2025-3-23 23:10
Machine Learning and Deep Learning in Crop Management—A Reviewoutput in a cost-effective manner. Researchers have used ML and DL techniques for different agriculture applications such as crop classification, automatic crop harvesting, pest and disease detection from the plant, weed detection, land cover classification, soil profiling, and animal welfare. This 作者: endarterectomy 時間: 2025-3-24 02:57 作者: 條約 時間: 2025-3-24 09:29
An Algorithmic Framework for Fusing Images from Satellites, Unmanned Aerial Vehicles (UAV), and Farmrial vehicles (UAVs) provide multispectral farm data with very high resolution spanning a few hundred square meters. In contrast, low-cost sensors and IoT sensors provide accurate spatial and time series data of land and soil characteristics spanning a few meters. However, in practice, each of these作者: Oversee 時間: 2025-3-24 12:15
Globally Scalable and Locally Adaptable Solutions for Agricultureg the productivity of crops and also optimizing the inputs, and at the same time sustaining the environmental resources. The ability of satellite data in precision farming has been made evident through several projects and research activities adopted across India. The past decade has seen a rapid in作者: 金盤是高原 時間: 2025-3-24 15:17
A Theoretical Framework of Agricultural Knowledge Management Process in the Indian Agriculture Contean also help improve the livelihood of rural communities in developing countries like India. However, little information on agriculture knowledge management processes and the ecosystem required for their implementation in the literature is available. This chapter attempts to derive a theoretical fra作者: Distribution 時間: 2025-3-24 20:47
Simple and Innovative Methods to Estimate Gross Primary Production and Transpiration of Crops: A RevDGs). New technology advancements and sources of information play a critical role in supporting agriculture to achieve the SDGs goals and increase production capabilities to meet rising food demands. Gross primary production (GPP) and transpiration (T) of crops are the largest carbon and water fluxe作者: 合唱隊 時間: 2025-3-25 00:05
Role of Virtual Plants in Digital Agricultureural plant models (FSPM), which are used to model an accurate plant shape and architecture and combines it with physiological processes. Static or dynamic FSPMs are a well-established approach to serve as a versatile tool for predicting crop growth patterns in response to variations in?environmental作者: aspect 時間: 2025-3-25 06:59 作者: ALOFT 時間: 2025-3-25 08:05 作者: Metastasis 時間: 2025-3-25 14:40 作者: BOOM 時間: 2025-3-25 17:55
Hyperspectral Remote Sensing for Agriculture Land Use and Land Cover Classificationf these emissions. Land use information is important for agriculture management, the information about which can be obtained by hyperspectral (HyS) remote sensing. The high spectral information from hyperspectral sensors can help in differentiating various LU/LC classes. In LULC, focus is to be laid作者: defenses 時間: 2025-3-25 21:34
Computer Vision Approaches for Plant Phenotypic Parameter Determinationr the challenges. In plant breeding, phenotypic trait measurement is necessary to develop improved crop varieties. Plant phenotyping refers to studying the plant‘s morphological and physiological characteristics. Plant phenotypic traits like the number of spikes/panicle in cereal crops and senescenc作者: Ejaculate 時間: 2025-3-26 02:04 作者: zonules 時間: 2025-3-26 08:21 作者: Influx 時間: 2025-3-26 10:41 作者: 緯線 時間: 2025-3-26 15:08
Role of Virtual Plants in Digital Agriculturetrategy to address the impending challenge of food security. In VP modeling, first step is to select the study crop and decide on what traits to be examined, then set up a field experiment, gathering temporal data on crop growth, perform statistical analysis of this data to explore the relationship 作者: 虛弱的神經 時間: 2025-3-26 18:18 作者: neutrophils 時間: 2025-3-26 22:00 作者: 評論性 時間: 2025-3-27 05:02
Hyperspectral Remote Sensing for Agriculture Land Use and Land Cover Classificationnd spectra with higher correlation for agriculture and built-up classes. Classification is performed using seven per pixel classifiers and one ensemble classifier. Support vector (SVM) and ensemble classifiers for both Hyperion and AVIRIS-NG HyS images have shown higher accuracy with accuracy percen作者: 把手 時間: 2025-3-27 09:21 作者: 誓言 時間: 2025-3-27 12:49 作者: Cantankerous 時間: 2025-3-27 15:47 作者: 愉快嗎 時間: 2025-3-27 18:43
Ahmad M. Salih,Mazni Omar,Azman Yasinitoring at a larger scale. This chapter focuses on the use of open-source high-resolution (in terms of spectral, spatial, and temporal resolution) satellite data; open source cloud-based platforms, and big data algorithms that are reforming agriculture. This book chapter will detail the available op作者: Alveoli 時間: 2025-3-27 23:11 作者: penance 時間: 2025-3-28 05:00 作者: Endometrium 時間: 2025-3-28 09:37 作者: 自作多情 時間: 2025-3-28 10:55
https://doi.org/10.1007/978-3-662-48634-4 CWR methods in India are mainly based on sparsely located in situ measurements and high-resolution remote sensing data, which limit the overall precision. To overcome the mentioned challenge, the deep learning architecture and soil moisture techniques have been used in this study to generate high-r作者: febrile 時間: 2025-3-28 15:22
Yuanyang Wang,Xiaohong Chen,Ling Yinnd spectra with higher correlation for agriculture and built-up classes. Classification is performed using seven per pixel classifiers and one ensemble classifier. Support vector (SVM) and ensemble classifiers for both Hyperion and AVIRIS-NG HyS images have shown higher accuracy with accuracy percen作者: formula 時間: 2025-3-28 20:54 作者: 不容置疑 時間: 2025-3-29 02:00 作者: 去掉 時間: 2025-3-29 06:09
Theresia Ratih Dewi Saputri,Seok-Won Leegricultural efficiencies and greening the energy sector by making room for sustainable renewable bioenergy crops. Efforts for climate change mitigation and adaptation with a focus on agriculture must come from transdisciplinary collaboration, which is often not easy and require one to come out of on作者: 顧客 時間: 2025-3-29 11:13 作者: cluster 時間: 2025-3-29 12:18 作者: HAIRY 時間: 2025-3-29 19:36
Yuanbang Li,Rong Peng,Bangchao Wangrial vehicles (UAVs) provide multispectral farm data with very high resolution spanning a few hundred square meters. In contrast, low-cost sensors and IoT sensors provide accurate spatial and time series data of land and soil characteristics spanning a few meters. However, in practice, each of these作者: Corroborate 時間: 2025-3-29 21:29 作者: 阻撓 時間: 2025-3-30 01:51
Colin M. Werner,Daniel M. Berryan also help improve the livelihood of rural communities in developing countries like India. However, little information on agriculture knowledge management processes and the ecosystem required for their implementation in the literature is available. This chapter attempts to derive a theoretical fra作者: 嚴重傷害 時間: 2025-3-30 05:51
Chunhui Wang,Wei Zhang,Haiyan Zhao,Zhi JinDGs). New technology advancements and sources of information play a critical role in supporting agriculture to achieve the SDGs goals and increase production capabilities to meet rising food demands. Gross primary production (GPP) and transpiration (T) of crops are the largest carbon and water fluxe作者: SPASM 時間: 2025-3-30 09:01 作者: DAUNT 時間: 2025-3-30 15:25
Muhammad Aufeef Chauhan,Christian W. Probstneity. Orchards and plantation crops contribute significantly to terrestrial C-pools but have not received adequate attention. Remote sensing (RS) with vegetation discrimination and monitoring capacity is critical to describe spatial C-pool variability. The natural forest in India has been significa作者: Harness 時間: 2025-3-30 19:14
Gitosree Khan,Sabnam Sengupta,Anirban Sarkarfor prediction, and it is a complex task with dependence on many parameters like weather, soil, and farm practices. The fusion of extra information can improve the prediction. Therefore, the chapter studies the impact of vegetation indices on wheat yield prediction using satellite images. The chapte作者: Vaginismus 時間: 2025-3-30 22:10
https://doi.org/10.1007/978-3-662-48634-4tion, application of fertilizers, etc. Irrigation is a very important phase of any crop cultivation practice. Irrigation scheduling, water management, crop forecasting, and demands precise crop-specific water requirements (CWR) which nowadays become extremely important for various crops grown under 作者: 轎車 時間: 2025-3-31 04:33
Yuanyang Wang,Xiaohong Chen,Ling Yinf these emissions. Land use information is important for agriculture management, the information about which can be obtained by hyperspectral (HyS) remote sensing. The high spectral information from hyperspectral sensors can help in differentiating various LU/LC classes. In LULC, focus is to be laid
