Economic and ecosystem issues associated with beekeeping may stem from bee mites rather than other bee diseases. The honey mites that stick to bees are small and possess a reddish-brown color, rendering it difficult to distinguish them with the naked eye. Objective and rapid technologies to detect bee mites are required. Image processing considerably improves detection performance. Therefore, this study proposes an image-processing method that can increase the detection performance of bee mites. A keypoint detection algorithm was implemented to identify keypoint location and frequencies in images of bees and bee mites. These parameters were analyzed to determine the rational measurement distance and image-processing. The change in the number of keypoints was analyzed by applying five-color model conversion, histogram normalization, and two-histogram equalization. The performance of the keypoints was verified by matching images with infested bees and mites. Among 30 given cases of image processing, the method applying normalization and equalization in the RGB color model image produced consistent quality data and was the most valid keypoint. Optimal image processing worked effectively in the measured 300 mm data in the range 300–1100 mm. The results of this study show that diverse image-processing techniques help to enhance the quality of bee mite detection significantly. This approach can be used in conjunction with an object detection deep-learning algorithm to monitor bee mites and diseases.

Beekeeping is facing a serious crisis due to climate change and diseases such as bee mites (Varroa destructor), which have led to declining populations, collapsing colonies, and reduced beekeeping productivity. Bee mites are small, reddish-brown in color, and difficult to distinguish from bees. Rapid bee mite detection techniques are essential for overcoming this crisis. This study developed a technology for recognizing bee mites and beekeeping objects in beecombs using the You Only Look Once (YOLO) object detection algorithm. The dataset was constructed by acquiring RGB images of beecombs containing mites. Regions of interest with a size of 640 × 640 pixels centered on the bee mites were extracted and labeled as seven classes: bee mites, bees, mite-infected bees, larvae, abnormal larvae, and cells. Image processing, data augmentation, and stratified data distribution methods were applied to enhance the object recognition performance. Four datasets were constructed using different augmentation and distribution strategies, including random and stratified sampling. The datasets were partitioned into training, testing, and validation sets in a 7:2:1 ratio, respectively. A YOLO-based model for the detection of bee mites and seven beekeeping-related objects was developed for each dataset. The F1 scores for the detection of bee mites and seven beekeeping-related objectives using the YOLO model based on original datasets were 94.1% and 91.9%, respectively. The model applied data augmentation, and stratified sampling achieved the highest performance, with F1 scores of 97.4% and 96.4% for the detection of bee mites and seven beekeeping-related objects, respectively. The results underscore the efficacy of using the YOLO architecture on RGB images of beecombs for simultaneously detecting bee mites and various beekeeping-related objects. This advanced mite detection method is expected to contribute significantly to the early identification of pests and disease outbreaks, offering a valuable tool for enhancing beekeeping practices.

Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.999 and an RMSE_P of 0.001 (Friedman test, p < 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems.

Varroa destructor (Bee mite) and Deformed Wing Virus are primary causes of honeybee colony collapse. This study developed an automated AI-based vision inspection system for detecting bee mites and deformed bees using the YOLO algorithm. The system integrates an RGB camera, a beecomb rotation motor, and an image transmission module to enable automated dual-sided image acquisition of the beecomb. The image characteristics of normal bees, bee mites, and deformed bees were analyzed, and YOLO-based object detection models were developed to classify them. Six YOLO models—based on YOLOv8 and YOLOv11 architectures across three model sizes (nano, small, and large)—were evaluated on 405 test images (6441 objects). The proposed system reduced the inspection time from 240 s required for manual method to 20 s per beecomb, achieving 12-fold efficiency improvement. Comparative analysis showed model-task specialization: YOLOv8l excelled in detecting small bee mites (F1: 92.5%, mAP[0.5]: 92.1%), while YOLOv11s achieved the highest performance for morphologically diverse deformed bees (F1: 95.1%). Error analysis indicated that detection performance was influenced by morphological characteristics. Deformed bee detection errors correlated with overlap in wing-to-body ratio: DB Type II exhibited 18.6% miss rate, while DB Type III achieved perfect detection. In bee mite detection, a sensitivity–specificity trade-off was observed: YOLOv11l had the lowest false negatives (2.5%) but highest false positives, while YOLOv8l demonstrated superior discrimination. These results demonstrate the practical potential of the proposed system for field deployment in apiaries, supporting early pest diagnosis and improved colony health management. The model-task specialization framework provides guidance for architecture selection based on object characteristics. Future work will focus on multi-location validation and real-time monitoring integration.

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images.