This study aims to improve the Agaricus bisporus detection efficiency and performance of harvesting robots in the complex environment of the mushroom growing house. Based on deep learning networks, an improved YOLOv5s algorithm was proposed for accurate A. bisporus detection. First, A. bisporus images collected in situ from the mushroom growing house were preprocessed and augmented to construct a dataset containing 810 images, which were divided into the training and test sets in the ratio of 8:2. Then, by introducing the Convolutional Block Attention Module (CBAM) into the backbone network of YOLOv5s and adopting the Mosaic image augmentation technique in training, the detection accuracy and robustness of the algorithm were improved. The experimental results showed that the improved algorithm had a recognition accuracy of 98%, a single-image processing time of 18 ms, an A. bisporus center point locating error of 0.40%, and a diameter measuring error of 1.08%. Compared with YOLOv5s and YOLOv7, the YOLOv5s-CBAM has better performance in recognition accuracy, center positioning, and diameter measurement. Therefore, the proposed algorithm is capable of accurate A. bisporus detection in the complex environment of the mushroom growing house.

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.

Seed tape sowing technology is a kind of crop cultivation technology based on a carrier. This technology first wraps crop seeds in a kind of carrier materials and makes them into seed tape. The seed tape is then laid down in farmland with special equipment. Seed tape planting has the advantages of accurate control of hill spacing, simplification of field sowing process and helps to implement order agriculture. Seed tape manufacturing and laying equipment are the core equipment of the technology and their working reliability directly affects the advantages of seed tape planting technology. Based on the research status of seed tape planting technology and equipment, this article made comparisons between the key technologies which include the method for seed tape manufacturing, seed tape sowing, furrowing, seed tape guiding technology, etc. In this paper, the basic problems of seed tape technology that still need further study are put forward. The future development of tape sowing technology and equipment are predicted as follows: the intelligent high precision and high-speed seed tape manufacturing equipment, the large intelligent integrated seed tape planter of “land preparation-seeding tape making-sowing”, the small and medium-sized mobile walk-behind planters, the application on the agricultural landscape and the crop transplanting. This study will be helpful to promote the further development of seed tape planting technology and provide a reference for the research of tape planting technology and equipment.

To solve the problems of high labor costs, a low weeding rate and a high seedling injury rate in the direct seeding of rice fields, this paper presents a reciprocating inter-row weeding machine for strip-seeded rice. The machine uses a combination of weeding wheels and weeding shovels to improve the efficiency of weeding between rice rows. Its reciprocating mechanism was designed and optimized. The simulation model of weeding teeth–paddy soil interaction was established in EDEM. The structural parameters of the weeding teeth were optimized, and the bending angle of the optimized weeding teeth was 55°. A prototype trial production and field tests were carried out. The results showed that the prototype’s inter-row weeding rate was between 80.2% and 85.3% and the seedling injury rate was between 3.5% and 5.1% when the prototype’s working speed was 1~3 km h⁻¹. The faster the speed of the prototype, the lower the inter-row weeding rate and the higher the seedling injury rate.

To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel’s reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding.

This study aimed to assess the feasibility of a novel weeding and fertilization scheme, namely, mechanical weeding plus a one-time deep application of a reduced amount of slow-release fertilizer for rice cultivation. The effects of the weeding and fertilization method on rice yield and quality were investigated using a split plot test as the research method. Two weeding methods, namely, chemical weeding (CW) and mechanical weeding (MW), and four fertilization methods were tested, including the conventional fertilization method (quantitative split broadcast application of fast-release N fertilizer (CK)), the quantitative split broadcast application of 80% fast-release N fertilizer (LCK), the one-time base application of slow-release fertilizer (SR), and the one-time deep application of 80% slow-release fertilizer (LSR). The results showed that the rice yield under MW with LSR treatment can maintain a high level—higher than 9.2 t ha⁻¹ per year. This was attributed to the slow-release fertilizer and deep fertilization, which increased the number of stems and tillers in the pre-fertility and spike rate, respectively, resulting in a high panicle number with a 20% reduction of N fertilizer. Furthermore, mechanical weeding improved the seed-setting rate, resulting in a higher number of grains per panicle, a higher panicle number, and an increased thousand-grain weight, thereby maintaining a high yield. At the same time, the quality of rice under MW with LSR treatment improved, specifically reflected in the significant improvement of the processing and appearance quality of rice, a slight increase in protein content, and a reduction in the amylose content, thereby improving its nutritional quality while maintaining good cooking quality.