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Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

In this study, we aim to validate the use of Artificial Intelligence of Things (AIoT) for deep learning-based detection of piglet crushing incidents by sows in pig farms, a leading cause of piglet mortality. To achieve this, we developed a deep neural network based on the You Only Look Once (YOLO) model, which was trained to detect piglet crushing events in real-time. We then optimized the weights of the YOLO model using TensorFlow Lite, TensorRT, and edge TPU to ensure efficient operation on AIoT devices with limited computational resources. Finally, we compared the performance of the deep learning-based piglet crushing detection algorithm on various single-board computers (SBCs), including the Jetson Nano, Raspberry Pi, and Coral Dev Board, to validate its suitability for real-world applications. [ABSTRACT FROM AUTHOR]