Emerging Sensor Technology in Agriculture

• Outros Autores: Fuentes, Sigfredo (Editor), Poblete-Echeverria, Carlos (Editor)
• Formato: Recurso Electrónico Capítulo de Livro
• Idioma: inglês
• Publicado em: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
• Assuntos: Research & information: general; Geography; apple orchards; modeling and simulation; unmanned aerial vehicles; fruit ripeness; ethylene gas detection; 3D crop modeling; remote sensing; on-ground sensing; depth images; parameter acquisition; capacitor sensor; deposit mass; pesticide droplets; formulations; ionization; CFD; airflow field test; monitoring method; spectral sensor; crop growth; computer vision; deep learning; image processing; pose estimation; animal detection; precision livestock; Citrus sinensis L. Osbeck; mechanical harvesting; acceleration sensor; vibration time; logistic regression; adaptive thresholding; fruit detection; parameter tuning; phenotype; phenotyping; phenomics; Triticum aestivum; water deficit; stress; infrared; leaf area index; cocoa beans; volatile compounds; artificial neural networks; VitiCanopy app; bushfires; infrared thermography; near-infrared spectroscopy; smoke taint; artificial intelligence; Kinect sensor; RGB; RGB-D; image segmentation; colour thresholding; bunch area; bunch volume; point cloud; mesh; surface reconstruction; image analysis; cluster morphology; machine learning; non-invasive sensing technologies; proximal sensing; precision viticulture; partial least square; support vector machine; Gaussian processes; soybean; pigeon pea; guar; tepary bean; n/a
• Acesso em linha: DOAB: download the publication; DOAB: description of the publication

Digital agriculture is gaining traction among scientists implementing different new and emerging sensor technologies to monitor complex soil-plant-atmosphere interactions in an accurate, cost-effective and user-friendly manner. This book presents some of the latest advances in this emerging area of...