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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 research. The diversity of applications in which digital agriculture can make an important difference in day-to-day farming decision making makes this discipline an important focus of research internationally.

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 --- 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