Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots.

During air-assisted spraying operations in orchards, the interaction between the droplets and the target leaves has a decisive influence on the retention of the droplets on the leaves and the final deposition state. Based on the observation of the final deposition effect of the droplets in the spray test, the retention state of the droplets on the leaves is divided into three categories: uniform distribution (hereinafter referred to as uniform), accumulation, and loss. During the initial interaction between the droplets and the leaves, the adhesion or sliding state of the droplets has an important influence on the final deposition state of the droplets, which is determined by the target leaf adhesion work in this paper. Based on obtaining the characteristic parameters of the leaf surface, a theoretical model of adhesion work related to parameters such as the contact angle, rough factor, and initial tilt angle of the leaf is established. Afterward, through the connection of the droplet coverage on the macro level, the establishment of the deposition state model of the droplet group on the leaf is completed. By conducting the experiment test based on the Box-Behnken design of response surface methodology (RSM), the droplet deposition states under the influence of the spray distance, fan outlet wind speed and droplet size were studied and compared with the predicted values. The test results show that the prediction accuracies of the three states of uniform, accumulation, and loss were 87.5%, 80%, and 100%, respectively. The results of the study indicate that the established prediction model can effectively predict the deposition states of droplets on leaves and provide a reference for the selection of spray operation parameters.

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

In all operations of banana post-harvesting, picking and transportation are the operations with the highest labor intensity and the highest production costs. The operation of banana de-handing is still in the traditional stage by manual cutting, which seriously impedes the development of the banana industry towards mechanization, automation and intelligence. This review summarizes and analyzes the mechanized technology research status and equipment application of the banana picking, transportation and de-handing operations in banana orchards around the world and proposes basic ideas and constructive suggestions for quickening the mechanized development of banana post-harvesting.

Comprehensive understanding of bruise damage caused by apple-to-apple impacts is beneficial to design a low-impact fruit capturing mechanism for mass (shake-and-catch) harvesting, as well as to design other fruit handling devices. This study quantified the bruising severity in ‘Jazz’ apples induced by different levels of impact upon various fruit surface locations. Impact experiments were carried out to analyze bruising patterns in three zones in a fruit surface, i.e., middle/cheek-to-top/stem, middle-to-middle and middle-to-bottom/calyx. Moving fruit and stationary fruit were impacted using a pendulum-type test device, and an equivalent drop height of fruit was calculated to provide a more practical measure for designing a catching surface. In each impact zone, seven different levels of impacts were applied respectively at seven different locations on the fruit surface. Those locations were evenly distributed along the circumferential direction in each of the three zones, and moving fruit was replaced after each impact test. The United States Department of Agriculture (USDA) standard was then used to estimate percentages of fruit in the Extra Fancy Class 1 (no bruising), Extra Fancy (a bruising area diameter ≤ 12.7 mm) and Fresh Market (a bruising area diameter ≤ 19 mm) grades. Results showed that fruit bruising severity increased in a non-linear manner with increasing drop height. It was also found that there existed significant differences in fruit bruising severity between stationary and moving fruit under different fruit-to-fruit impact zones. The bottom zone showed the least bruising sensitivity, followed by the middle zone which was statistically similar to the same in the top zone. The results suggested that the free drop height need to be <3 cm to keep from fruit bruising caused by apple-to-apple impact at a negligible level for ‘Jazz’ apples.