Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.

In recent years, there has been a shift towards a greater demand for more nutritious and healthier foods, emphasizing the role of diets in human well-being. Edible Alliums, including common onions, garlic, chives and green onions, are staples in diverse cuisines worldwide and are valued specifically for their culinary versatility, distinct flavors and nutritional and medicinal properties. Green onions are widely cultivated and traded as a spicy vegetable. The mild, onion-like flavor makes the crop a pleasant addition to various dishes, serving as a staple ingredient in many world cuisines, particularly in Eastern Asian countries such as China, Japan and the Republic of Korea. The green pseudostems, leaves and non-developed bulbs of green onions are utilized in salads, stir-fries, garnishes and a myriad of culinary preparations. Additionally, green onions have a rich historical background in traditional medicine and diets, capturing the attention of chefs and the general public. The status of the crop as an important food, its culinary diversity and its nutraceutical and therapeutic value make it a subject of great interest in research. Therefore, the present review has examined the distribution, culinary, nutritional and therapeutic significance of green onions, highlighting the health benefits derived from the consumption of diets with this aromatic vegetable crop as a constituent.

Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution ($3840\times 2160$) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users.

Vespa velutina is an ecosystem disruptor that causes annual damage worth KRW 170 billion (USD 137 million) to the South Korean beekeeping industry. Due to its strong fertility and high-lying habitat, it is difficult to control. This study aimed to develop a system for the control of V. velutina nests using drones for detection and tracking the real-time location of the nests. Vespa velutina nest image data were acquired in Buan-gun and Wanju-gun (Jeollabuk-do), and artificial intelligence learning was conducted using YOLO-v5. Drone image resolutions of 640, 1280, 1920, and 3840 pixels were compared and analyzed. The 3840-pixel resolution model was selected, as it had no false detections for the verification image and showed the best detection performance, with a precision of 100%, recall of 92.5%, accuracy of 99.7%, and an F1 score of 96.1%. A computer (Jetson Xavier), real-time kinematics module, long-term evolution modem, and camera were installed on the drone to acquire real-time location data and images. Vespa velutina nest detection and location data were delivered to the user via artificial intelligence analysis. Utilizing a drone flight speed of 1 m/s and maintaining an altitude of 25 m, flight experiments were conducted near Gyeongcheon-myeon, Wanju-gun, Jeollabuk-do. A total of four V. velutina nests were successfully located. Further research is needed on the detection accuracy of artificial intelligence in relation to objects that require altitude-dependent variations in drone-assisted exploration. Moreover, the potential applicability of these research findings to diverse domains is of interest.