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Automatic transplanting of seedlings is of great significance to vegetable cultivation factories. Accurate and efficient identification of healthy seedlings is the fundamental process of automatic transplanting. This study proposed a computer vision-based identification framework of healthy seedlings. Vegetable seedlings were planted in trays in the form of potted seedlings. Two-color index operators were proposed for image preprocessing of potted seedlings. An optimal thresholding method based on the genetic algorithm and the three-dimensional block-matching algorithm (BM3D) was developed to denoise and segment the image of potted seedlings. The leaf area of the potted seedling was measured by machine vision technology to detect the growing status and position information of the potted seedling. Therefore, a smart identification framework of healthy vegetable seedlings (SIHVS) was constructed to identify healthy potted seedlings. By comparing the identification accuracy of 273 potted seedlings images, the identification accuracy of the proposed method is 94.33%, which is higher than 89.37% obtained by the comparison method.

HIGHLIGHTS The mechanism of straw smashing was analyzed. Factors such as the speed of the cutter shaft, the number of blades, the thickness, and the inclination angle had a greater impact on the theoretical length of the straw section after crushing. Based on the straw crushing mechanism, the structure of the crushing chamber was designed. The corn stalk crushing and sending device was trial-produced and field experiments were carried out. ABSTRACT . In order to further improve the crushing quality of corn stalks, this research designs a corn stalk crushing and throwing device. First, it introduced the overall structure and working principle, and analyzed the crushing mechanism of corn stalks to obtain the main factors affecting its crushing performance. Then, the crushing blade in the crushing chamber was designed to determine that the number of crushing blades was 10. Kinematics and dynamic balance analysis, and the establishment of a mathematical model, the speed range of the crushing cutter shaft was 530~900 r/min. On this basis, the ADAMS motion simulation software was used to measure the change curves of the restraint force, runout, and acceleration of the shaft end with different speeds of the crushing cutter shaft. The simulation analysis finally determined that when the speed of the crushing knife shaft was 700 r/min, and the working performance of the device was better and meets the requirements of dynamic balance. Finally, the verification test was carried out, and the result shows: when the speed of the crushing knife shaft was 700 r/min, the qualified rate of corn stalk crushing length was 93.65%. Compared with the original silage corn crushing and throwing device, the performance had increased by 4.78%. It meets the standard of corn stalk crushing operation, which can provide a theoretical basis and scientific basis for the design and optimization of corn stalk crushing and returning equipment. Keywords: Crushing mechanism, Dynamic balance, Motion simulation, Silage corn.

This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage.

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In order to improve the quality of corn sowing and fertilizer utilization, reduce labor costs, this paper aimed at the traditional tillage fertilizer machine to improve. The seeding and fertilizing machine with adjustable flow is designed by integrating particle fertilizer sensor and CAN bus transmission technology. It is mainly composed of seed and fertilizer discharging mechanism, ditch and soil covering mechanism, and leakage seeding and blockage monitoring system. Fertilizer drop signal can be obtained in real time by detecting the flow sensor outside the fertilizer discharge pipe. The speed of the stepper motor can be adjusted after signal processing, which can also realize the alarm of missing sowing and blocking light, and realize the precise variable sowing. The field experiment results show that the maximum relative error of seed leakage rate monitoring is 3.75%, and the alarm accuracy of fertilizer clogging is high. This machine can reduce the intensity of manual operation, and the quality of operation can be effectively monitored and controlled, can effectively reduce the production cost, has better practicability and economy.