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This paper proposes a modeling method of maize in threshing. The static friction coefficient and rolling resistance coefficient of the maize grain were measured using the slope method. The maize grain stacking angle test was designed using the central composite design response surface test. A regression model was established based on the simulation results to find the best combination. The results suggested that the modeling method proposed in this paper was effective in improving the accuracy of maize grain simulation compared with previous methods. Furthermore, this paper presents a method to verify the feasibility and reliability of the maize grain cob discrete element model using the distribution of grain in the granary and the final removal rate as the verification method. The results of the actually simulated threshing test were analyzed using a Wilcoxon signed-rank test, heat map analysis, and the Spearman's rank correlation coefficient. It was found that the DEM model of maize cob is suitable for simulating the maize threshing process. This can aid in further research on the subject. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPADCL and ear leaf (EL) SPADEL were established, and a linear fitting analysis was conducted combining the measured SPADCL values and SPADEL values on the ground. The results showed that for SPADCL, the R2 of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPADEL, the R2 of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance and light interference in navigation path recognition in a field environment. This technique is based on the Res2net50 model and incorporates the Squeeze-and-Excitation Networks (SE) attention mechanism to focus on the key aspects of the image. The empty space pyramid pooling module is presented to further extract high-level semantic data and enhance the network's capacity for fine-grained representation. A skip connection is used to combine the high-level semantic characteristics and low-level textural features that are extracted. The results of the ridge prediction are then obtained, followed by the realization of the final image segmentation, through sampling. Lastly, the navigation line is fitted once the navigation feature points have been retrieved using the resulting ridge segmentation mask. The outcomes of the experiment reveal that: the Mean Intersection over Union (MIOU) and F-measure values of the inter-ridge navigation path extraction approach suggested in this paper are increased by 0.157 and 0.061, respectively, compared with the Res2net50 network. Under various illumination situations, the average pixel error is 8.27 pixels and the average angle error is 1.395°. This technique is appropriate for ridge operations and can successfully increase network prediction model accuracy. [ABSTRACT FROM AUTHOR]

This study designed a cam-linked planetary gear system automatic seedling picking mechanism to address unstable operation and issues of high damage to the substrate caused by the picking mechanism of the dryland vegetable seedling transplanter. Through an analysis of the kinematic principle of the structure and the establishment of the kinematic model, computer-aided analysis software was developed using Visual Basic 6.0. Consequently, a set of structural parameter values satisfying the target trajectory was derived employing the human–computer interaction method, and the 3D model was designed. The model was imported into Adams for kinematic simulation, and the seedling picking mechanism's trajectory during the operation was obtained through simulation. Modal analysis of the model was performed using Ansys, and the first six-order modal vibration patterns and modal frequencies of the seedling picking mechanism were obtained under the simulated working environment. The results confirmed that no resonance occurred during the operation. Comparisons of the seedling picking needle trajectory with the idle test revealed that the theoretical, simulated, and test trajectories were approximately identical. This proved the reliability of the theoretical design of the seedling picking mechanism, the machining of the parts, and the test bench construction. The success rates of seedling picking were 97.66, 96.09, 93.75, and 90.63% at 90, 100, 110, and 120 plants/min, respectively, with rates of substrate damage of 4.43%, 6.73%, 9.57%, and 14.37%, respectively. Thus, the experimental results confirmed that the operating parameters of the cam-linked planetary gear system seedling picking mechanism satisfied the design requirements. [ABSTRACT FROM AUTHOR]

To address the problem of low efficiency and automatically sense the phenotypic characteristics of wheat seeds, a wheat seed phenotype detection device was designed to predict thousand seed weight. Five commonly used varieties of wheat seeds were selected for the study, and a wheat seed phenotype detection system was built with a 2 mm sampling hole plate. Grayscale, image segmentation, area filtering and other methods were used to process the image in order to extract and analyse the correlation between thousand seed weight and seven phenotypic characteristics: wheat seed area, perimeter, long axis, short axis, ellipticity, rectangularity, and elongation. The results showed that different varieties of wheat seeds were significantly correlated with different phenotypic characteristics. Among them, the area and short axis for Luomai 26; the area, long axis, short axis, perimeter, and rectangularity for Jinqiang 11; the area and perimeter for Zhoumai 22; the area of Luomai 42; the area, short axis, and perimeter for Bainong 207 were significantly correlated with the thousand seed weight. A multiple linear regression model of thousand seed weight prediction was developed by selecting the significantly correlated phenotypic characteristic. The models showed that the R2 values of the thousand seed weight prediction models for Jinqiang 11 and Bainong 207 were 0.853 and 0.757, respectively; and the R2 values for Luomai 26, Zhoumai 22, and Luomai 42 were less than 0.5. Subsequently, PCA-MLR was used to build a thousand seed weight prediction model, and K-fold cross-validation was used for comparative analysis. Afterwards, three kinds of wheat seeds with 40–50 g thousand seed weight were selected to validate the model. The validation results showed that the more significantly correlated the phenotypic parameters were, the higher the accuracy of the thousand seed weight prediction model. The study provided a set of detection devices and methods for the rapid acquisition of the phenotypic characteristics of wheat seeds and thousand seed weight prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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 (δZH). The optimization results showed that to achieve a minimum δZH (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. [ABSTRACT FROM AUTHOR]

Subsoiling is an effective practice to promote better soil water infiltration and crop growth. The information regarding the variation and persistence of subsoiling effects with different soil disturbance on soil properties and crop growth was absent in previous research. In this study, the effects of wing mounting height (h) (75–155 mm) during subsoiling on soil disturbance at various layers, soil properties and crop performance were investigated using in situ field experiments from 2019 to 2021 in winter wheat–summer maize rotations. The experimental field soil was covered with crop stubble and crop straw was removed before subsoiling or sowing the next crop. The analysis of variance (ANOVA) was used to assess different subsoiling treatment effects on tested variables, including soil moisture content, bulk density, plant diameter, plant height, dry root weight, root number, yield, and yield components of crops. Means between subsoiling treatments were compared using Duncan's multiple range tests. Based on ANOVA outputs, the h significantly (p < 0.05) affected soil moisture content in the first growing season of winter wheat (WW1), soil bulk density, number of roots and panicle number and grain yield of WW1, and thousand kernel weight (TKW) of both WW1 and summer maize (SM). Decreasing h overall gave better soil properties and crop performance. Decreased subsoiling effects were found on aspects of insignificant difference in subsequent grain yield of SM and winter wheat in the second growing season (WW2). These findings had implications for designing higher-performance winged subsoilers, as well as selection of subsoiling frequency. [ABSTRACT FROM AUTHOR]