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

Inspection

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(xué)科排名




書目名稱Digital Ecosystem for Innovation in Agriculture網(wǎng)絡(luò)公開度




書目名稱Digital Ecosystem for Innovation in Agriculture網(wǎng)絡(luò)公開度學(xué)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(xué)科排名




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書目名稱Digital Ecosystem for Innovation in Agriculture年度引用學(xué)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(xué)科排名

genesis

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

acclimate

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.

Conjuction

HEAVY

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.

DEFER

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.

DEFER

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.

蜈蚣

cuticle

減少

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.