The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot.

The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms’ low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.

The pollination during bees' foraging is vital to continue species on the Earth. However, the losses of bee colonies are being more extensive problems in recent decades, which have seriously impacted the global environment but have not received enough attention from human beings yet. Based on widely investigating detectable features, this article presents an IoT-based system for long-term and extensive monitoring of bee colonies. Multiple features, including temperature and humidity inside the hive, bee combs' weight, bee colony's sounds, and a number of bees passing through the hive entrance, can be detected hourly by the proposed system. Furthermore, a four-month practical monitoring indicates that the system can continuously operate without human intervention and the data can reveal the activity and growth of bee colonies. Especially, a swarming activity was successfully observed by the data, which shows a significant potential to recognize various activities of bee colonies. The highlights of this system are the capabilities of detecting diverse features, transmitting data wirelessly and that with a strong self-support ability. Because of these advantages, the system can operate for an exceeding long time without maintenance, which will largely benefit system deployment in the field. In the visible future, this system could contribute to the study of bee's behavior and the development of precision beekeeping.

In this paper, we propose a tree trunk and obstacle detection method in a semistructured apple orchard environment based on improved YOLOv5s, with an aim to improve the real-time detection performance. The improvement includes using the K-means clustering algorithm to calculate anchor frame and adding the Squeeze-and-Excitation module and 10% pruning operation to ensure both detection accuracy and speed. Images of apple orchards in different seasons and under different light conditions are collected to better simulate the actual operating environment. The Gradient-weighted Class Activation Map technology is used to visualize the performance of YOLOv5s network with and without improvement to increase interpretability of improved network on detection accuracy. The detected tree trunk can then be used to calculate the traveling route of an orchard carrier platform, where the centroid coordinates of the identified trunk anchor are fitted by the least square method to obtain the endpoint of the next time traveling rout. The mean average precision values of the proposed model in spring, summer, autumn, and winter were 95.61%, 98.37%, 96.53%, and 89.61%, respectively. The model size of the improved model is reduced by 13.6 MB, and the accuracy and average accuracy on the test set are increased by 5.60% and 1.30%, respectively. The average detection time is 33 ms, which meets the requirements of real-time detection of an orchard carrier platform.

To investigate the effects of corona discharge field treatment of different electric field strength with the different polarities on the vigor of millet seeds, the positive and negative corona discharge fields generated with arc-shaped prick electrodes and plate electrodes are different for millet seeds respectively. The treatment voltage was (2 × n kV, n = 1, 2...10) and the time treated was 2 min. The results showed that after treatment by the corona discharge field, the germination potential, germination rate, germination index and vigor index of millet seeds increased to different degrees compared with the control. The optimal treatment voltages of the positive and negative corona discharge field were 12 kV. Under the optimal treatment voltage, the germination indexes (germination potential, germination rate, germination index, vigor index) of millet seeds were significantly different from the control (p<0.01), and the millet seeds treated by the positive corona discharge field compared with the control group, the germination potential, germination rate, germination index and vigor index increased by 21.2%, 23.0%, 19.9% and 23.2% respectively. The germination indexes of the millet seeds treated by the negative corona discharge field increased by 29.1%, 28.5%, 26.9% and 32.1%. It was also found that under the same treatment voltage, the electrical conductivity of the leachates of the millet seed treated with the negative corona discharge field was lower than that of the positive, which indicated that the negative corona discharge field would repair the cell membrane more effectively. According to various indicators, it can be concluded that the negative corona discharge field treatment was better than the positive.