Digital Innovations in Agriculture

© 2022 by the authors; CC BY-NC-ND license

lameness; biomarkers; dairy cows; precision dairy farming; tractor; powershift transmission; fuel consumption; load level; simulation model; automatic milking system; reproduction; blood; metabolic profile; arecanut; PlanetScope satellite image; random forest algorithm; feature optimization; area extraction; deep learning; aquaculture automation; computer vision; chicken detection; lameness; inline biomarkers; fresh dairy cows; rumination; locomotion; precision dairy farming; digital farming; deep learning; image analysis; plant area; growth pattern; rice yield; vegetation indices; hyper-temporal data; PLSR; cattle face detection; RetinaNet; deep learning; precision livestock; precision agriculture; management zones; remote sensing; Sentinel-2; clustering; winter wheat; drought; digital agriculture; VIS-NIR spectroscopy; screening algorithm; estimation model; HJ-1A imagery; artificial neural networks; essential oil; fennel; medicinal plant; trans-anethole; stepwise regression; bagworms; image segmentation; color features; deep learning; faster R-CNN; false color; machine learning; deep learning; image processing; hyperspectral image analysis; computer vision; sow; image processing; behavior; precision livestock; animal welfare; remote sensing; time series; data fusion; feature extraction; dynamic time warping; crop phenological information; Africa; knowledge systems; climate change; agriculture; adaptation; large cranberry; mechanical properties; cranberry compression; water content; mathematical modelling; machine learning; complex background; pigs; DeepLab V3+; attention mechanism; count; FAIR data; findable; accessible; interoperable; reusable; sustainability; soybean; yield; sensitivity analysis; vegetation period; weather conditions; artificial neural network; Internet of Things (IoT); LPWAN; LoRaWAN; Omnet++; FLoRa; agriculture; rural applications; YOLOX; small target scale; loss function; feature integration; fruit picking; redundant discrete wavelet transform; tea; convolutional neural network; classification; soil; hyperspectral; iron oxide; spectra transform; fractional order differential; external grading system; oil palm FFB; machine learning; supervised classifiers; quality inspection; remote sensing; granular fertilizer applicator; fertilization decision; multi-objective optimization; NSGA-III; breakage rate; hyperspectral image; partial least squares regression; prediction models; root mean square error of prediction; standard normal variate; total anthycyanins; total carotenoids; total chlorophylls; total glucosinolates; total phenolics; computer vision; CCT; cotton pest attack; whitefly attack; deep learning; precision agriculture; wheat quality; fuzzy quality certification model; fresh agricultural products; time window; path optimization; genetic algorithm; crop management; sustainable agriculture; smart farming; internet-of-things (IoT); advanced agriculture practices; issues and problems; digital soil mapping; soil process units; soil parameter space; machine learning; unsupervised classification; precision dairy farming; sensors technology; dairy cows; pig; temperature; humidity; GRU; prediction; corn leaf disease; real scene; lightweight model; DFCANet; machine learning; artificial intelligence; yield prediction; blueberry; bivariate fertilizer applicator; opening length; rotational speed; control combination determination; PID parameter tuning; artificial neural networks; multiple linear regression; protein prediction; pea; sensitivity analysis; weather conditions; automatic disease detection; Ganoderma boninense; hyperspectral imaging; deep learning; bagworm; hyperspectral image; deep learning; transfer learning; instar stage; plant health detection; precision agriculture; deep learning; object detection; YOLOv5; path planning; ACO; IWOA; electric tractor; electronic-agriculture; digital farming; machine learning; yield prediction; remote sensing; short message service (SMS); Web; pea; seeds yield prediction; ANN; MLR; sensitivity analysis; artificial neural networks; empirical data analysis; statistical methods; agriculture; machine learning; aeroponics; hydroculture; neural network; regression; biomass; fresh weight; relative growth rate; image analysis; food safety; image phenotyping; Lactuca sativa L.; vegetables; planthoppers; convolutional neural network; machine vision; paddy cultivation; n/a