Abstract: Safflower is one of the most important specialty economic crops in the world. Safflower blossoms are harvested continuously for 3–5 times. Untimely picking can affect the opening of filaments in the next crop, resulting in reduced filament production and serious economic losses. However, the safflower images collected by picking robots were affected by lighting conditions or complex backgrounds. The existing segmentation algorithms have the problem of insufficient segmentation or over-segmentation, because of the small safflower size and localization area. Therefore, combining improved particle swarm optimization (PSO) with a rotated rectangle algorithm based on the filament-necking localization method for safflower-picking robots was proposed. The Rcomponent image in RGB color space was extracted as a preprocessing sample by combining the color features of safflower. The inertia weights in the PSO algorithm are improved to enhance the performance of the algorithm. An adaptive nonlinear function is introduced to search for the optimal threshold and initially segmented to obtain the binary image. Then, the barycenter and minimum outer rectangle of the contour were set up using the rotated rectangle algorithm based on the geometric features of the filaments. The circular region of interest (CirROI) of filament-necking is determined. The Zhang-Suen refinement algorithm was used for skeleton extraction to design an algorithm for localizing the picking point of safflower filaments. To test safflower images collected in complex environments, the results of average processing time were 0.14 s, and the average relative target area error rate was 19.33 %. Moreover, the localization accuracy of the picking point was 89.75 %. The filamentnecking localization method provides a theoretical basis and experimental data support for damage reduction and efficient harvesting of safflower filaments.
Keywords: Picking robots , Visual localization , Image segmentation , Improved PSO algorithm , Future farming
