Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study.

Uniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting, as it affects subsequent field management and the crop yield. However, current transplanters are not able to meet this requirement due to the lack of accurate navigation systems. In this study, a plant row detection algorithm was developed to serve as a navigation system of a rice transplanter. The algorithm was based on the convolutional neural network (CNN) to identify and locate rice seedlings from field images. The agglomerative hierarchical clustering (AHC) was used to group rice seedlings into seedling rows which were then used to determine the navigation parameters. The accuracies of the navigation parameters were evaluated using test images. Results showed that the CNN-based algorithm successfully detected rice seedlings from field images and generated a reference line which was used to determine navigation parameters (lateral distance and travel angle). Compared with mean absolute errors (MAE) test results, the CNN-based algorithm resulted in a deviation of 8.5 mm for the lateral distance and 0.50° for the travel angle, over the six intra-row seedling spacings tested. Relative to the test results, the CNN-based algorithm had 62% lower error for the lateral distance and 57% lower error for the travel angle when compared to a classical algorithm. These results demonstrated that the proposed algorithm had reasonably good accuracy and can be used for the rice transplanter navigation in real-time.

No abstract

Accurate and automatic real-time recognition of shrimp with and without shells is the key to improve the efficiency of automatic peeling machines and reduce the labor cost. Existing methods cannot obtain excellent accuracy in the absence of target samples because there are too many species of shrimp to obtain a complete dataset. In this paper, we propose a tactile recognition method with universal applicability. First, we obtained tactile data, e.g., the texture and hardness of the surface of the shrimp, through a novel layout using the same type of sensors, and constructed fusion features based on the energy and nonstationary volatility (ENSV). Second, the ENSV features were input to an adaptive recognition boundary model (ARBM) for training to obtain the recognition boundary of shrimp with and without shells. Finally, the effectiveness of the proposed model was verified by comparison with other tactile models. The method was tested with different species of shrimp and the results were 88.2%, 87.0%, and 89.4%, respectively. The recognition accuracy of the overall, shrimp with shells and shrimp without shells verified the generalizability of the proposed method. This method can help to improve the efficiency of automatic peeling machines and reduce the labor cost.

Most of the commercially-available pot seedling nursery machines are incompatible with soft-pot-trays and are labor-intensive and low in productivity. A soft-pot-tray automatic embedding system was developed in this study to achieve automatic embedding of the soft pot tray into the hard tray following sowing and covering with soil. A control system was constructed using the Arduino program development environment. An embedded-hard-tray automatic lowering mechanism and conveyor-belt-based pot-tray embedding system were designed. Dynamics analysis was conducted to derive an equation to describe the embedding process of the soft pot tray into the embedded hard tray. A prototype of the soft-pot-tray automatic embedding system was manufactured and tested. The analytical equation suggested that a minimum linear velocity of 0.86 m/s was required for a complete embedding process. The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%. The successful tray embedding rate was 100% and the seed exposure rate was less than 1% with a linear velocity of the conveyor belt of 0.92 m/s. The experiment findings agreed well with the analytical results. The proposed soft-pot-tray automatic embedding system satisfied the technical specifications for a light-economical pot seedling nursery machine.