Defective citrus fruits are manually sorted at the moment, which is a time-consuming and cost-expensive process with unsatisfactory accuracy. In this paper, we introduce a deep learning-based vision system implemented on a citrus processing line for fast on-line sorting. For the citrus fruits rotating randomly on the conveyor, a convolutional neural network-based detector was developed to detect and temporarily classify the defective ones, and a SORT algorithm-based tracker was adopted to record the classification information along their paths. The true categories of the citrus fruits were identified through the tracked historical information, resulting in high detection precision of 93.6%. Moreover, the linear Kalman filter model was applied to predict the future path of the fruits, which can be used to guide the robot arms to pick out the defective ones. Ultimately, this research presents a practical solution to realize on-line citrus sorting featuring low costs, high efficiency, and accuracy.

How to non-destructively and quickly estimate the storage time of citrus fruit is necessary and urgent for freshness control in the fruit market. As a feasibility study, we present a non-destructive method for storage time prediction of Newhall navel oranges by investigating the characteristics of the rind oil glands in this paper. Through the observation using a digital microscope, the oil glands were divided into three types and the change of their proportions could indicate the rind status as well as the storage time. Images of the rind of the oranges were taken in intervals of 10 days for 40 days, and they were used to train and test the proposed prediction models based on K-Nearest Neighbors (KNN) and deep learning algorithms, respectively. The KNN-based model demonstrated explicit features for storage time prediction based on the gland characteristics and reached a high accuracy of 93.0%, and the deep learning-based model attained an even higher accuracy of 96.0% due to its strong adaptability and robustness. The workflow presented can be readily replicated to develop non-destructive methods to predict the storage time of other types of citrus fruit with similar oil gland characteristics in different storage conditions featuring high efficiency and accuracy.

The ocean contains huge valuable resources, due to the huge pressure in the deep water and the danger of diving, human development is very little. In this paper, we design a small underwater observation robot, which can replace human beings to dive into deep water, detect the deepwater area and sample the water quality. Following the principle of fish diving, the robot uses the piston bucket to pump water and drain water to change its gravity to achieve floating and diving. The main propeller at the tail provides the main driving force. The robot turns by propellers on both sides working at different speeds and propellers can change the thrust direction to provide assist force in the process of diving and floating. The structure design of the tumbler(the design of anti-roll) ensures the anti-interference ability of the robot to the wind and wave. In addition, the robot can be equipped with cameras, depth sensors, temperature and humidity sensors, and other sensors to achieve efficient underwater data sampling. The depth sensor, propeller, and pumping piston are used to control the hovering depth of the robot. The ROV can record good quality of video if provided with proper video graphic tool. The design of ROV which we have conceptualize, is handy and can be powered by 24V DC power which can be easily available at remote location. The experimental results verify the structural reliability of the underwater detection robot and provide a new mobile mechanism platform and a new idea for deep-sea exploration and scientific research.

No abstract