Modern people who value healthy eating habits have shown increasing interest in plum (Prunus mume) fruits, primarily owing to their nutritiousness and proven efficacy. As consumption increases, it becomes important to monitor work to prevent Prunus mume fruits from falling out. Moreover, determining the growth status of Prunus mume is also crucial and is attracting increasing attention. In this study, convolutional neural network (CNN)-based deep learning object detection was developed using RGBD images collected from Prunus mume farms. These RGBD images consider various environments, including the depth information of objects in the outdoor field. A faster region-based convolutional neural network (R-CNN), EfficientDet, Retinanet, and Single Shot Multibox Detector (SSD) were applied for detection, and the performance of all models was estimated by comparing their respective computing speeds and average precisions (APs). The test results show that the EfficientDet model is the most accurate, and SSD MobileNet is the fastest among the four models. In addition, the algorithm was developed to acquire the growth status of P. mume fruits by applying the coordinates and score values of bounding boxes to the depth map. Compared to the diameters of the artificial Prunus mume fruits used as the experimental group, the calculated diameters were very similar to those of the artificial objects. Collectively, the results demonstrate that the CNN-based deep learning Prunus mume detection and growth estimation method can be applied to real farmlands.

HighlightsAn NIR-Vis hyperspectral imaging approach was developed to predict the viability of rice seeds.Through multi-step accelerated aging, seed lots in various states were used for the experiments.Models using spectral information and spectral-spatial information of hyperspectral images were used and compared.Abstract. Rice is one of the world’s most important food crops, and rice seed viability is an important factor in rice crop production. In this study, a visible–near infrared (vis–NIR) hyperspectral imaging system and spectral–spatial information modeling are used to predict the viability of rice seeds. Experimental samples are prepared using seeds harvested in two different years and artificially aged for various periods. Vis-NIR hyperspectral acquisition and germination tests of the prepared seed samples are performed. Partial least square (PLS)–discriminant analysis, a support vector machine (SVM), a PLS–SVM, a PLS–artificial neural network, and a one-dimensional–convolutional neural network (CNN) for the mean spectra of seeds, as well as a CNN, a PLS–CNN, and dual branch networks for the hyperspectral images of the seeds are applied for viability prediction modeling. Result shows that an accuracy of approximately 90% and high f1 scores can be obtained in most models. Furthermore, it is confirmed that models using spectral and spatial information can classify hard samples more effectively. Keywords: Deep learning, Hyperspectral images, Rice, Seed, Spectroscopy, Viability.