Accurate recognition of Agaricus bisporus is a prerequisite for precise automatic harvesting in a factory environment. Aimed at segmenting mushrooms adhering together from the complex background, this paper proposes a watershed-based segmentation recognition algorithm for A. bisporus. First, the foreground of A. bisporus is extracted via Otsu threshold segmentation and morphological operations. Then, a preliminary segmentation algorithm and a novel iterative marker generation method are proposed to prepare watershed markers. On this basis, a marker-controlled watershed algorithm is adopted to segment and recognize A. bisporus individuals. All the algorithms are implemented based on OpenCV (Open Source Computer Vision) libraries. Tests on images of A. bisporus collected at the cultivation bed show that the average correct recognition rate of the proposed algorithm is 95.7%, the average diameter measurement error is 1.15%, and the average coordinate deviation rate is 1.43%. The average processing time is 705.7 ms per single image, satisfying the real-time constraints based on 1 image/s. The proposed algorithm performed better than the current Circle Hough Transform (OpenCV’s implementation). It is convenient and easy to operate, providing a sound basis for subsequent research on mechanized harvesting equipment for A. bisporus.
Targeting the problems of low precision and heavy workload in conventional screening of filled and unfilled grain in single-plant rice testing, a screening system for filled and unfilled grain was designed based on the coupling of the wind and gravity fields. In this study, the motion state of filled and unfilled grain in the flow field and the results of screening were analyzed and combined with aerodynamics. In order to reveal the influence law of the structural and working parameters of the screening system on the screening performance and determine the optimal parameter combination, this study conducted a quadratic regression orthogonal rotating center combination test with four factors and three levels based on the DEM–CFD coupling method. The relationship between air inlet wind speed, air cross-section shape, horizontal distance, vertical distance, and removal rate was studied. The results showed that, in a certain range, the removal rate was positively correlated with the section width of the outlet, positively correlated with the wind speed, and negatively correlated with the vertical distance and horizontal distance of the seed-drop outlet. The optimization results showed that, when the section width of the outlet was 75.44 mm, the wind speed was 8.90 m·s−1, the transverse distance was 198.78 mm, and, when the vertical distance was 34.87 mm, the screening rate of the screening system could reach 99.6%.
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