Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
With the rapid development of the turtle breeding industry in China, the demand for automated turtle sorting is increasing. The automatic sorting of Chinese softshell turtles mainly consists of three parts: visual recognition, weight prediction, and individual sorting. This paper focuses on two aspects, i.e., visual recognition and weight prediction, and a novel method for the object detection and weight prediction of Chinese softshell turtles is proposed. In the individual sorting process, computer vision technology is used to estimate the weight of Chinese softshell turtles and classify them by weight. For the visual recognition of the body parts of Chinese softshell turtles, a color space model is proposed in this paper to separate the turtles from the background effectively. By applying multiple linear regression analysis for modeling, the relationship between the weight and morphological parameters of Chinese softshell turtles is obtained, which can be used to estimate the weight of turtles well. An improved deep learning object detection network is used to extract the features of the plastron and carapace of the Chinese softshell turtles, achieving excellent detection results. The mAP of the improved network reached 96.23%, which can meet the requirements for the accurate identification of the body parts of Chinese softshell turtles....
Efficient irrigation water use directly affects crop productivity as demand increases for various agricultural products due to population growth worldwide. While technologies are being developed in various fields, it has become desirable to develop automatic irrigation systems to reduce the waste of water caused by traditional irrigation processes. This paper presents a novel approach to an automated irrigation system based on a non-contact computer vision system to enhance the irrigation process and reduce the need for human intervention. The proposed system is based on a stand-alone Raspberry Pi camera imaging system mounted at an agricultural research facility which monitors changes in soil color by capturing images sequentially and processing captured images with no involvement from the facility’s staff. Two types of soil samples (sand soil and peat moss soil) were utilized in this study under three different scenarios, including dusty, sunny, and cloudy conditions of wet soil and dry soil, to take control of irrigation decisions. A relay, pump, and power bank were used to achieve the stability of the power source and supply it with regular power to avoid the interruption of electricity....
In froth flotation, one of the pivotal metrics employed to evaluate the flotation efficacy is the clean ash content, given its widely acknowledged status as a paramount gauge of coal quality. Leveraging deep learning and computer vision, our study achieved the dynamic recognition of coal flotation froth, a key element for predicting and controlling the ash content in coal concentrate. A comprehensive dataset, assembled from 90 froth flotation videos, provided 16,200 images for analysis. These images revealed key froth characteristics including bubble diameter, quantity, brightness, and bursting rate. We employed Keras to build a comprehensive deep neural network model, incorporating multiple features and mixed data inputs, and subsequently trained it with a rigorous 10-fold cross-validation strategy. Our model was evaluated using robust metrics including the mean squared error, mean absolute error, and root mean squared error, demonstrating a high precision with respective values of 0.003017%, 0.053385%, and 0.042640%. With this innovative approach, our work significantly enhances the accuracy of ash content prediction and provides an important breakthrough for the intelligent advancement and efficiency of froth flotation processes in the coal industry....
Aerospace T-shaped machined parts are varied and have small structural differences. Manual identification has the problems of low efficiency and low accuracy. In order to realize efficient and accurate classification of aerospace machining parts, we built an image acquisition platform. To improve the edge detail extraction capability, we improved the edge detection algorithm based on deep learning. Furthermore, we employed the VisionTrain software to train recognition classification models for both large classes and subclasses. We then established a crossgranularity image classification process using VisionMaster software. Experimental results show that the improved edge detection algorithm in this paper is better than the existing common algorithm. The system achieves the goal of quickly and accurately recognizing all 60 machined parts....
The Robotics Vision Lab of Northwest Nazarene University has developed the Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 s. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted in images with the lowest noise. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94% compared to 88% during the daytime operations....
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