Abstract: Abstract:Straw mulching has been an efficient solution to reduce soil loss in environmental protection and sustainable development in modern agriculture. Therefore, a rapid detection of straw coverage can contribute to the efficiency and accuracy in the process of straw mulching. In this study, a novel algorithm was proposed to optimize large-scale image segmentation for the aerial image of straw coverage during straw mulching. An artificial bee colony survey multi-objective grey wolf optimization algorithm (AS-MOGWO) was used to upgrade via introducing the design concept of multi-objective integration. Specifically, an external archive of multi-objective grey wolf optimization algorithm (MOGWO) was added into the differential evolution (DE) GWO, and the search strategy of observed bees in artificial colony algorithm. The DE algorithm can be used to solve the problem that the traditional gray wolf optimization algorithm (GWO) is easy to fall into the local optimal and the slow processing speed. Extending to multi-objective can also improve the accuracy of multi-threshold image segmentation. The Observation phase of artificial bee colony algorithm (ABC) can be used to compare the solution of problem, and further to enhance the stability and optimization ability of algorithm. The DE-GWO algorithm was extended from single target to multi-target DE-MOGWO, thereby to achieve multi-objective optimization. The accuracy of multi-threshold image segmentation was greatly improved, while, the algorithm was enhanced to extract and classify different ground objects in the collected images. The observation phase of ABC algorithm was added in the detection of straw coverage, further to improve the quality and processing speed of automatic image segmentation. The stability and optimization ability of algorithm can be enhanced after the integration of various methods. The upgraded algorithm inherited the automatic segmentation of DE-GWO, while gained the efficient convergence of AS-MOGWO, indicating an improved stability and processing speed for image segmentation. An optimal threshold was set using the gray-scale histogram of straw image, then to segment the images, and finally to calculate the number of pixels in each part and the coverage of straw. The experimental results showed that the matching error was less than 8% between the DE-AS-MOGWO optimization algorithm and the manual measurement method. Compared with the PSO, GWO, DE-GWO, and DE-MOGWO algorithms, the average matching rate of DE-AS-MOGWO improved 4.967, 3.617, 2.188 and 3.404 percentage point, respectively, whereas, the average error rate reduced 0.168, 0.131, 0.089 and 0.116 percentage point, respectively. Furthermore, the algorithm time reduced 82%, 84%, 17% and 32%, respectively. A software system was also developed for the area detection of straw coverage based on the proposed algorithm, where the straw covering area and straw coverage rate can be calculated from the acquisition area of aerial images. The GWO, DE-GWO, DE-MOGWO and DE-AS-MOGWO algorithms can also be selected for the comparison of different results. The DE-AS-MOGWO algorithm can produce a better segmentation, while processing with large-scale UAV images in a short time, indicating an excellent applicability under various conditions in the images of straw coverage. The finding can provide a promising potential way to improve the segmentation accuracy for the detection of straw coverage in modern agriculture.