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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]

Current rice planting operations are mainly transplanting and direct seeding. However, many operational procedures involving transplanting use poor quality of machinery resulting in high labour costs and low working efficiency. Here a no-tube seeding (NTS) method is proposed and the effects of technical parameters (travel speed, seeding wheel rotational speed and ground clearance) on the seeding quality (accuracy, dispersion and uniformity) are investigated using a single factor bench test. A multifactor bench test was carried out to optimise the technical parameters based on the requirements of different seeding rate. The optimal value of the travel speed was constant at 0.80 m s−1 at different seeding rates, the optimised range of seeding wheel rotational speed and ground clearance is 17.5–23.53 r min−1 and 81.2–85.6 mm, respectively. A field experimental comparison between NTS, traditional tube seeding (TTS) showed that the seeding quality of NTS was significantly better than that of TTS. Compared with transplanting, rice growth status at booting stage for NTS and TTS was not significantly different with rows closed. However, the number of panicles per unit area was greater which was beneficial to the formation of high yield. The maximum and average yield of NTS increased by 10.1% and 6.7% compared with TTS and compared with transplanting it maximum and average yield increased by 11.6% and 9.3% respectively. These results indicate that NTS is feasible and efficient method. • Lower ground clearance promotes higher seeding accuracy. • Higher ground clearance promotes greater seeding dispersion. • Higher seed wheel rotational speed promotes higher seeding uniformity. • Travel speed has limited effect on seeding quality. • The comprehensive benefit of NTS is better than that of TTS and transplanting. [ABSTRACT FROM AUTHOR]