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Agaricus bisporus (white button mushrooms) is nutritious edible fungus, and the whiteness of the mushroom is an important indicator to evaluate its freshness and quality. To realize the accurate measurement of A. bisporus' whiteness, this paper proposes a white measurement method of A. bisporus based on image analysis. An imaging system was built with a D65 standard light source, and a nonlinear color calibration model was constructed. The image RGB value was converted into a CIE-XYZ tri-stimulus value, and then the CIE Ganz whiteness formula was used to accurately determine the whiteness. Based on colorimeter measurement results, the image system was calibrated using 8 Gy-level cards, and the whiteness of A. bisporus with various whiteness grades was measured. The measurements of machine vision and colorimeter had a high correlation (r = 0.99), and the color difference ΔE*ab < 2. For 4 groups of A. bisporus with different whiteness grades, the measurement results of machine vision among the four groups were significantly different (P < 0.01). At the same time, the whiteness of A. bisporus under different storage conditions was measured by machine vision. There was a significant correlation between the storage time and whiteness, which indicated that the whiteness value determined by machine vision can be used to quantitatively evaluate the freshness of A. bisporus. [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]

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. [ABSTRACT FROM AUTHOR]

Objective: To realize the regulation of the position of corn seed planting in precision farming, an intelligent monitoring system is designed for corn seeding based on machine vision and the Genetic Algorithm-optimized Back Propagation (GABP) algorithm. Methods: Based on the research on precision positioning seeding technology, comprehensive application of sensors, Proportional Integral Derivative (PID) controllers, and other technologies, combined with modern optimization algorithms, the online dynamic calibration controls of line spacing and plant spacing are implemented. Based on the machine vision and GABP algorithm, a test platform for the seeding effect detection system is designed to provide a reference for further precision seeding operations. GA can obtain better initial network weights and thresholds and find the optimal individual through selection, crossover, and mutation operations; that is, the optimal initial weight of the Back Propagation (BP) neural network. Field experiments verify the seeding performance of the precision corn planter and the accuracy of the seeding monitoring system. Results: 1. The deviation between the average value of the six precision positioning seeding experiments of corn under the random disturbance signal and the ideal value of the distance is less than or equal to 0.5 cm; the deviation between the average value of the six precision positioning seeding experiments of corn under the sine wave disturbance signal (1 Hz) is less than or equal to 0.4 cm; the qualified rate of grain distance reaches 100%. 2. The precision control index, replay index, and missed index of the designed corn precision seeding intelligent control system have all reached the national standard. During the operation of the seeder, an alarm of the seeder leaking occurred, and the buzzer sounded and the screen displayed 100 times each; therefore, the reliability of the alarm system is 100%. Conclusion: The intelligent corn seeder designed based on precision positioning seeding technology can reduce the seeding rate of the seeder and ensure the stability of the seed spacing effectively. Based on the machine vision and GABP algorithm, the seeding effect detection system can provide a reference for the further realization of precision seeding operations. [ABSTRACT FROM AUTHOR]

Cow lameness is a complex locomotion behaviour, and effective characteristics are the key to identifying early lameness. Machine learning methods were used to quantify the movement of the hooves and head of cows, and they showed the possibility of using the head- and back-hoof linkage features extracted from motion analysis to classify early lameness. Firstly, the key points of the body in the side-view walking videos were tracked by DeepLabCut (DLC). Based on the curves of hoof movement and head swing, the head-hoof linkage pattern was analysed. Second, the curve of cow's back was extracted and the back-hoof linkage feature was built when the hoof touched the ground. At the same time, the movement curves were analysed to extract five other gait characteristics, including asymmetry of stride length, tracking up, asymmetry in the proportion of supporting phase, landing speed, and moving speed. Multiple models were trained on a dataset containing 212 videos, and 10-fold cross-validation was used to verify the performance of the algorithm. The classification performance was improved by optimising the model and algorithm. Overall accuracy and recall were 89.2% and 90.7%, respectively. The scores of the importance of features calculated by the Chi-square test showed that the head- and back-hoof linkage features play an important role in the classification of early lame cows. The hooves and their linkage features with the head and back of cows proposed in this paper can thus effectively detect early lameness in cows. • DeepLabCut was used to track four hooves and head of walking cow. • Head- and back-hoof linkage features was analysed. • Seven lameness indicators were used to build the model. • The recall of classification for early lame cows was 90.7%. [ABSTRACT FROM AUTHOR]

• An online monitoring system for the uprightness of transplanting machines based on machine vision and deep learning is constructed is constructed. • A seedling planting status dataset is constructed and an efficient algorithm YOLO-RDS for the detection of the planting posture of seedlings is proposed. • Improved traditional (HBB) object detection box, the introduction of angular predictor headers, and the RWBF detection head constructed. • Integrated development of a seedling quality monitoring system and field experiments conducted. During the mechanized transplanting process, the planting angle and standing posture of seedlings are essential factors in evaluating the survival of the planting, and the uprightness of the planting is an essential indicator in evaluating the planting quality. With the gradual expansion of mechanization, the traditional method of manually checking the uprightness of mechanized planting has gradually become inapplicable. To address this problem, an online monitoring system for the uprightness of transplanting machines based on machine vision and deep learning is constructed. The system mainly consists of an image acquisition device, a vehicle-mounted computer, and an interactive interface. Furthermore, to improve the identification errors caused by the unstructured environment in the field, a field planting uprightness monitoring device was designed to integrate LED light sources, photoelectric sensors, a CCD camera, and light hoods. An efficient algorithm YOLO-RDS for the detection of the planting posture of seedlings is proposed using a single-stage detection paradigm. First, the traditional (HBB) target detection box is improved and the circle smooth label is introduced to adapt to different seedling planting inclined postures. Secondly, the RWBF detection head network was constructed, and the stem incline angle was extracted using adaptive rotation to calculate the actual incline angle of the seedlings in three-dimensional space. Experimental results show that YOLO-RDS is better than the classic rotating target detection method, the average accuracy (mAP) is 97%, and FPS is 22. By encapsulating the algorithm, a seedling planting uprightness monitoring system was integrated and developed. Field experiments show that the average relative error rate of uprightness identification monitored by the system is 3.09%, the accuracy rate is 92.56%, and the absolute angle error is 0°-3°. The system has good accuracy and stability, meets the requirements for seedling uprightness monitoring, and can provide technical support for the quality evaluation of seedling transplanter operations. [ABSTRACT FROM AUTHOR]

• The edge recognition technology of seedlings is combined with the end effector of the transplantation manipulator to plan the path, thereby reducing the transplantation loss. • During the pipeline operation of the seedling transplanting, only the time for the first seedling edge recognition is increased. Seedling transplantation is the key link in the automation of protected horticulture, and the transplantation quality will directly affect the crop yield. In the previous research, our research group developed a prototype of a low-loss transplantation robot for plug seedlings based on machine vision. On this basis, a method of planning seedling path by integrating seedling edge recognition technology and end effector of transplanting manipulator is proposed to reduce the loss of seedling transplantation. Firstly, the RGB image and depth image of the whole row of seedlings were obtained from the side of the plug seedling with Intel RealSense D415 depth camera. Then, the extreme point coordinates E (X cb , Y ca) of seedling edge were obtained by edge recognition algorithm, and the coordinate value of z-axis was known. Taking point E as the starting point the transplanting manipulator picked up seedlings from the E point in an "L" path, which can reduce the damage of transplanting manipulator to seedling stems and leaves during seedling taking, thereby reducing the transplanting loss. Through three-factor four-level orthogonal test, it was determined that the distance between the depth camera and the plug tray was 600 mm, the height from the horizontal plane was 135 mm, and the light intensity was level 7. Then, a calibration test of extreme points on the edge of plug seedlings was carried out, and the calibration success rate was 98.4%. The deviation of X coordinate was within 5 mm, and the average ratio of deviation was 12.8%. The deviation of Y coordinate was within 4 mm, and the average ratio of deviation was 3%. The quality of image and the accuracy of depth information were the main causes of the deviation. Then, the comparative experiments of routine transplantation group, fixed transplantation group and machine vision transplantation group were carried out. The results showed that compared with the conventional transplantation group, the injury rate of machine vision group decreased by 11.11%, and the average time of single transplantation increased by 0.029 s. Compared with the fixed transplantation group, the injury rate increased by 0.46%, and the average transplantation time decreased by 0.238 s. Because the edge recognition time and the transplantation time intersected and did not interfere, only the time for the first seedling edge recognition was increased in the pipeline operation. Therefore, this method can reduce the damage rate while ensuring the efficiency. This study can provide a reference for reducing the injury rate of leafy vegetable seedlings transplantation. [ABSTRACT FROM AUTHOR]