Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review.

Weeding is a key link in agricultural production. Intelligent mechanical weeding is recognized as environmentally friendly, and it profoundly alleviates labor intensity compared with manual hand weeding. While intelligent mechanical weeding can be implemented only when a large number of disciplines are intersected and integrated. This article reviewed two important aspects of intelligent mechanical weeding. The first one was detection technology for crops and weeds. The contact sensors, non-contact sensors and machine vision play pivotal roles in supporting crop detection, which are used for guiding the movements of mechanical weeding executive parts. The second one was mechanical weeding executive part, which include hoes, spring teeth, fingers, brushes, swing and rotational executive parts, these parts were created to adapt to different soil conditions and crop agronomy. It is a fact that intelligent mechanical weeding is not widely applied yet, this review also analyzed the related reasons. We found that compared with the biochemical sprayer, intelligent mechanical weeding has two inevitable limitations: The higher technology cost and lower working efficiency. And some conclusions were commented objectively in the end.

Mechanical and friction properties of agricultural plastic film during autumn harvest period of cotton in Xinjiang, China.

Agricultural plastic films have caused serious plastic pollution. There are many studies that consider mechanical recycling an appropriate system for the recovery of post-consumption agricultural mulch film. The recovery effect of plastic film depends on the mechanical properties, the level of dirtiness of the post-consumption film, and the recycling process itself. In this study, the mechanical properties of four types of polyethylene plastic films with a thickness of 8, 10, 12, and 10 µm, weather-resistant, commonly used in Xinjiang cotton fields, were tested. As well as the friction coefficient between the film and soil, the cotton stalk, boll shell, and leaf with different moisture contents were measured. Then, the self-propelled straw chopping and residual film recycling combined machine collected the four types of mulch films. The results showed that the longitudinal mechanical properties of the plastic film were greater than the transversal ones, with the exception of the nominal tensile strain at break, and the tensile characteristics of the mulching film covered with soil were greater than those without soil. The dynamic or static friction coefficient between the film and the contact material had a linear relationship with the moisture content of the material. During the recycling operation, the better the mechanical properties of the plastic film, the higher the pick-up rate of the mulch film. The maximum longitudinal tensile force of 12-µm plastic film was 3.42 N, and the nominal tensile strain at break was 303.09%. The pick-up rate reached more than 93% when the 12-µm plastic film was recovered in autumn, which effectively reduced the residue of plastic film coverage in the current year. Moreover, the more soil that was present on the much film, the greater the soil content of the recycled film roll, and the stalk content also increased, but the change was small. The research provides a reference for the mechanical and the friction features of agricultural plastic film in Xinjiang, and provides a theoretical basis for the formulation of standards for film thickness and mechanical properties, as well as the design and optimization of a residual film collecting machine in the cotton field.

Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning.

The widespread use of unmanned aerial vehicles (UAV) is significant for the effective management of orchards in the context of precision agriculture. To reduce the traditional mode of continuous spraying, variable target spraying machines require detailed information about tree canopy. Although deep learning methods have been widely used in the fields of identifying individual trees, there are still phenomena of branches extending and shadows preventing segmenting edges of tree canopy precisely. Hence, a methodology (MPAPR R-CNN) for the high-precision segment method of apple trees in high-density cultivation orchards by low-altitude visible light images captured is proposed. Mask R-CNN with a path augmentation feature pyramid network (PAFPN) and PointRend algorithm was used as the base segmentation algorithm to output the precise boundaries of the apple tree canopy, which addresses the over- and under-sampling issues encountered in the pixel labeling tasks. The proposed method was tested on another miniature map of the orchard. The average precision (AP) was selected to evaluate the metric of the proposed model. The results showed that with the help of training with the PAFPN and PointRend backbone head that AP_seg and AP_box score improved by 8.96% and 8.37%, respectively. It can be concluded that our algorithm could better capture features of the canopy edges, it could improve the accuracy of the edges of canopy segmentation results.

Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation.

To accurately evaluate residual plastic film pollution in pre-sowing cotton fields, a method based on modified U-Net model was proposed in this research. Images of pre-sowing cotton fields were collected using UAV imaging from different heights under different weather conditions. Residual films were manually labelled, and the degree of residual film pollution was defined based on the residual film coverage rate. The modified U-Net model for evaluating residual film pollution was built by simplifying the U-Net model framework and introducing the inception module, and the evaluation results were compared to those of the U-Net, SegNet, and FCN models. The segmentation results showed that the modified U-Net model had the best performance, with a mean intersection over union (MIOU) of 87.53%. The segmentation results on images of cloudy days were better than those on images of sunny days, with accuracy gradually decreasing with increasing image-acquiring height. The evaluation results of residual film pollution showed that the modified U-Net model outperformed the other models. The coefficient of determination(R2), root mean square error (RMSE), mean relative error (MRE) and average evaluation time per image of the modified U-Net model on the CPU were 0.9849, 0.0563, 5.33% and 4.85 s, respectively. The results indicate that UAV imaging combined with the modified U-Net model can accurately evaluate residual film pollution. This study provides technical support for the rapid and accurate evaluation of residual plastic film pollution in pre-sowing cotton fields.