Precision dairy farming technology is widely used to improve the management efficiency and reduce cost in large-scale dairy farms. Machine vision systems are non-contact technologies to obtain individual and behavioral information from animals. However, the accuracy of image-based individual identification of dairy cows is still inadequate, which limits the application of machine vision technologies in large-scale dairy farms. There are three key problems in dairy cattle identification based on images and biometrics: (1) the biometrics of different dairy cattle may be similar; (2) the complex shooting environment leads to the instability of image quality; and (3) for the end-to-end identification method, the identity of each cow corresponds to a pattern, and the increase in the number of cows will lead to a rapid increase in the number of outputs and parameters of the identification model. To solve the above problems, this paper proposes a cascaded dairy individual cow identification method based on DeepOtsu and EfficientNet, which can realize a breakthrough in dairy cow group identification accuracy and speed by binarization and cascaded classification of dairy cow body pattern images. The specific implementation steps of the proposed method are as follows. First, the YOLOX model was used to locate the trunk of the cow in the side-looking walking image to obtain the body pattern image, and then, the DeepOtsu model was used to binarize the body pattern image. After that, primary classification was carried out according to the proportion of black pixels in the binary image; then, for each subcategory obtained by the primary classification, the EfficientNet-B1 model was used for secondary classification to achieve accurate and rapid identification of dairy cows. A total of 11,800 side-looking walking images of 118 cows were used to construct the dataset; and the training set, validation set, and test set were constructed at a ratio of 5:3:2. The test results showed that the binarization segmentation accuracy of the body pattern image is 0.932, and the overall identification accuracy of the individual cow identification method is 0.985. The total processing time of a single image is 0.433 s. The proposed method outperforms the end-to-end dairy individual cow identification method in terms of efficiency and training speed. This study provides a new method for the identification of individual dairy cattle in large-scale dairy farms.

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R²), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m², and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture.

Agaricus bisporus is widely cultivated worldwide due to its considerable economic benefits. The increasingly prominent contradiction between production and labor shortage necessitates the urgent replacement of human workers with intelligent picking technology and equipment. Therefore, a pressure-stabilized flexible end-effector was designed to achieve the rapid and low-loss picking of Agaricus bisporus. The dimensions of the end-effector were determined by measuring the external parameters of Agaricus bisporus. A mechanics model of the end-effector was constructed to analyze the picking process theoretically, and the pre-experiments identified the key performance factors of the end-effector: the thickness of the flexible membrane, the particle filler material, and the pressure stability. A series of experiments were conducted to investigate the factors mentioned above concerning the performance of the end-effector. The results show that the adsorption effect is best when the thickness of the flexible membrane is 0.9 mm, and the particle diameter is 200 mesh quartz. To control the adsorption force of the end-effector accurately during the picking process, a low-cost adsorption force-adjustment system was designed, and the stability of the system was verified. The experimental results showed that the device improved the stability of the adsorption force during the operation of the picking system by 84.71%. An experiment was conducted on the picking of Agaricus bisporus using the designed end-effector. The success rate of picking with the end-effector was 98.50%, and the picking damage rate was 2.50%.

Mechanical subsoiling is an effective practice to promote better water infiltration and crop root development. The bending subsoiling tool (BST) is a primary subsoiling tool and is used to remove soil compaction and restore soil productivity. In this study, a discrete element model was developed and validated using laboratory soil bin tests to investigate the effects of the mounting angle of the BST (5°–33°) on soil disturbance behaviors and draft forces. The results show that the upheaval, failure and fragmentation of soil was achieved by successive shearing, uplifting, extrusion, tension and turning actions from the cutting share and cambered shank of the BST. Increasing soil depths gave smaller soil disturbance ranges in lateral, forward and upward directions. With an increase in mounting angle, both the draft force and soil rupture distance ratio initially decreased and then increased, whereas the soil loosening efficiency initially increased and then decreased. Overall, increasing the mounting angle of the BST from 5° to 33° gave a greater soil surface flatness that increased rapidly when the mounting angle increased from 26° to 33°. Appropriately increasing mounting angle of the BST from 5° to 26° could lift more moist soil from the deep seed and middle layers (5.0–15.5% increase) into the shallow seed layer (depth of <50 mm) without seriously affecting the mixing of the deep layer and other layers. Considering both the soil disturbance characteristics and draft forces, a mounting angle of 26° appeared to outperform the other angles.

Seedling transplanting is an important part of vegetable mechanized production in modern agriculture. After the seedlings are cultivated on a large scale by the nursery tray, they are planted into the field by the transplanter. However, unlike manual transplanting, transplanter is unable to judge the status of seedlings in the hole during seedling planting, which leads to problems such as damaged seedlings and empty holes being picked in the same order and planted into the field, resulting in yield reduction and missed planting. Aiming at this problem, we designed a seedling selective planting control system for vegetable transplanter which includes vision unit, seedling picking mechanism, seedling feeding mechanism, planting mechanism, pneumatic push rod unit, limit sensor, industrial computer and logic controller. We used asymmetrical light to construct visual identification scenes for planting conditions, which suppresses environmental disturbances. Based on the intersection operation of mask and image, a fast framework of tray hole location and seedling identification (FHLSI) was proposed combined with FCM segmentation algorithm. The vision unit provides the transplanting system with information on the status of the holes to be transplanted. Based on the information, planting system chooses the healthy seedlings for transplanting, improving the survival rate and quality of transplanting. The results show that the proposed visual method has an average accuracy of 92.35% for identification with the selective planting control system of seedlings and improves the transplanting quality by 15.4%.

Discrete element modelling (DEM) parameters are of great importance for the accurate prediction of soil properties and disturbance. This study aimed to provide an efficient method for accurately determining the DEM parameters of a paddy soil with a high moisture content (45.66%). The DEM parameters of the paddy soil modelled using the Hertz–Mindlin with JKR contact model were determined by using the Plackett–Burman (PB), steepest ascent, and central composite tests. The accuracies of the developed DEM models were evaluated using actual slumping tests. Based on the PB test, the surface energy of soil, coefficients of soil–soil rolling friction, and coefficients of soil–steel static friction exerted larger influences on the overall relative error (δ_ZH). The optimization results showed that to achieve a minimum δ_ZH (5.96%), the surface energy of soil, the coefficients of soil–soil rolling friction, and the coefficients of soil–steel static friction should be 0.808 J m⁻², 0.11, and 0.6, respectively. The optimized DEM model had an overall relative error of 7.27% with a coefficient of variation of 1.32%, indicating that the DEM parameters of the calibrated paddy soil had good accuracy.