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Copyright of Journal of Japan Society for Fuzzy Theory & Intelligent Informatics is the property of Japan Society for Fuzzy Theory & Intelligent Informatics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

From a field-management perspective, monitoring flower buds and fruits in blueberry cultivation is essential. However, blueberry cultivation in Japan is mainly small-scale and manual, which hinders real-time monitoring of the entire field. Here, with the ultimate aim of automating monitoring of blueberry fields, we examined the feasibility of estimating the number of flower buds and fruits per bush by using object detection through deep learning, specifically employing YOLOv5 and YOLOv8 models. First, we detected and classified normal and frost-damaged flower buds and unripe and ripe fruits at various resolutions using a DSLR camera. Then, we estimated the numbers of flower buds and fruits in the images by using a simple regression formula. The results showed high accuracy of the automatic counts of normal buds, ripe fruits, and unripe fruits in the images but low accuracy for frost-damaged buds. For flower-bud detection, the highest average precision (AP) index was 0.755 and the lowest regression error index mean absolute percentage error (MAPE) was 10.79%. For fruit detection, the highest AP was 0.815 and the lowest MAPE was 23.84%. Furthermore, we tested the possibility of estimating the total number of fruits, including those hidden behind leaves, from the number of fruits visible in the images. We found a positive correlation between the number of fruits in the images and the total number of fruits (r=0.96, p<0.001), with a simple regression error MAPE of 21.2%. Also, the degree of leaf coverage markedly affected the estimation error. However, we found that the imaging direction was inconsequential and image collection from both sides of the row was unnecessary. Finally, we examined potential field applications of this automatic flower bud and fruit counting and classification technology, such as early yield prediction (during the flower bud phase), understanding the spatial variation of pollination effects by bees, and determining the optimum harvest time and harvest sequence. [ABSTRACT FROM AUTHOR]

Field surveys of rice planthoppers that are important pests of rice in Japan (Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus) are conducted by expert researchers in prefectural pest-control stations. Surveys for classification and counting by species and developmental stage involve visual inspection of individual planthoppers captured on sticky boards. This work requires special skill and takes considerable time, especially during severe outbreaks of rice planthoppers, and an alternative method for rapidly counting rice planthoppers is required. We aimed to develop an automatic system for classification and counting of rice planthoppers using an image-based object-detection technique that uses deep learning. Sticky boards were used to capture planthoppers in paddy fields and in the laboratory. High-resolution images of the boards were acquired using a flatbed scanner. Eighteen classes of annotations were applied to these images, and data sets for deep learning were prepared. We used You Only Look Once (YOLO) as the deep learning algorithm for object detection and verified the classification accuracy for each data set and each class combined with learning conditions, such as the image input size. Following the training process, the algorithm was applied to a test dataset, and the highest mean average precision was 0.79 and the corresponding F1-score was 0.88. [ABSTRACT FROM AUTHOR]