Titlebook: Digital Agriculture; A Solution for Susta P. M. Priyadarshan,Shri Mohan Jain,Jameel M. Al-Kh Book 2024 The Editor(s) (if applicable) and Th

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Consolidating Financial Supervisiond by ocean and land phenomenon as well as the information systems, in Indian context, designed for application by varied users. Data and information derived by range of sensors on board Indian satellites are discussed, and latest understanding of vulnerability patterns in varied land cover contexts

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Consolidating Financial Supervisionteristics for agriculture monitoring, especially regarding the visible, near-infrared, and red-edge parts of the spectrum, which can be related to biomass, canopy vigor, or chlorophyll content and subsequently be matched with agronomic features. Regarding data access and coverage, openly accessible

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New Directions in Labour Regulation Research learning methods applied to yield prediction in exponentially increasing number of studies. We first describe the conventional feature-based machine learning techniques after which the use of several deep learning methods for yield prediction is considered.

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https://doi.org/10.1057/9780230307834ata analytics and machine learning to uncover key stress-responsive traits and genes. Through this synthesis, we emphasize the transformative impact of phenomics on breeding programs and its pivotal role in developing stress-resilient crop varieties, ensuring sustainable agricultural productivity in

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New Directions in Labour Regulation Researchng, early- and high-throughput phenotyping, rapid population development, etc. would boost the genetic analysis and increase the rate of genetic gain in the cultivar development of the crop plants. In the backdrop of increasing food and nutrition demands, gains in crop improvement need to be increas

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Book 2024lly collected information is converted into computer-readable language. Digital innovations thus allow real-time analysis, machine learning, and artificial intelligence (AI) that manage massive amount of data, also known as big data. Accordingly, digital agriculture affords greater potential for sus