Titlebook: Digital Ecosystem for Innovation in Agriculture; Sanjay Chaudhary,Chandrashekhar M. Biradar,Mehul S Book 2023 The Editor(s) (if applicable

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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

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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

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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

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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

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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