Pig behavior can provide information about the barn environment conditions, the adequacy of food and water, health, welfare and productivity. It is of great significance to detect the behavior in time to improve the health level of pigs and economic benefits. This paper reviews the latest technologies for pig behavior detection, and systematically summarizes the characteristics and limitations of some representative pig behavior detection methods in practical applications, including sensor-based pig behavior detection, machine vision-based pig behavior detection, and sound-based pig behavior detection, etc. The next research direction of pig behavior detection is discussed.
It is difficult to distinguish different maize varieties accurately by naked eyes or simple instruments because of their similar appearance. However near-infrared spectroscopy (NIRS) provides a quick, accurate and non-destructive way to analyze of maize varieties. Near-infrared spectral images of four types of maize seeds are collected in the wavelength range of 1100nm-1900nm. The near-infrared spectra are preprocessed by standard normalization transform and wavelength preselection. Eighty near-infrared spectra are obtained for each type of maize seeds, 60 of which are randomly selected as the training set and the remaining 20 spectra as the testing set. Then, support vector machine (SVM), SVM-Genetic algorithm (SVM-GA), backpropagation (BP) neural network, and principal component analysis-K nearest neighbor algorithm (PCA-KNN) are established for the variety identification. The average accurate identification rates are 93.75%, 97.50%, 92.50% and 87.50% respectively. The SVM-GA model shows better performance for near-infrared spectra of maize seeds, and the classification accuracy increases nearly by 4%. This work provides an effective method for rapid automatic classification of maize varieties.
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