Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
With the development of machine vision and spectral detection technology, online sorting of fruit internal and external quality has been developed rapidly. However, for spherical fruits, it is difficult to obtain full surface images during sorting, so it is difficult to accurately calculate the size of the surface defects and the ratio of defects to the full surface. In this paper, a full surface line scanning image acquisition device for spherical fruit is proposed. Based on this device, the line scanning hyperspectral image of spherical fruit is collected, and the original image is extracted by feature extraction and background removal. Next, the isometric projection image and the equivalent projection image of the feature image is obtained through cartography projection transformation; The number of feature pixels in the original feature image, the isometric projection image, the equivalent projection image, and the width of the original feature image are used as input parameters to predict the actual defect area with the help of the shallow neural network. In this paper, the equipment and method are verified using three test balls with different diameters and pasting different sizes of identification blocks at different positions on their surfaces. The experimental results show that the prediction accuracy R of the test set of the model is 0.9937, and the RMSE is 0.3391 cm2. It can be seen that the method has good prediction accuracy, which can provide a reference for the hyperspectral on‐line sorting method of external quality of spherical fruit.Practical applicationThis method provides an effective solution for the quality sorting production line of spherical fruits. In addition to agricultural product quality testing and food quality testing, similar to the detection of industrial products such as ball balls, the scheme provided in this manuscript can also be used as one of the options.The method proposed in this manuscript is suitable for all kinds of line scanning equipment, including hyperspectral imager and laser profilometer.
With the development of machine vision and spectral detection technology, online sorting of fruit internal and external quality has been developed rapidly. However, for spherical fruits, it is difficult to obtain full surface images during sorting, so it is difficult to accurately calculate the size of the surface defects and the ratio of defects to the full surface. In this paper, a full surface line scanning image acquisition device for spherical fruit is proposed. Based on this device, the line scanning hyperspectral image of spherical fruit is collected, and the original image is extracted by feature extraction and background removal. Next, the isometric projection image and the equivalent projection image of the feature image is obtained through cartography projection transformation; The number of feature pixels in the original feature image, the isometric projection image, the equivalent projection image, and the width of the original feature image are used as input parameters to predict the actual defect area with the help of the shallow neural network. In this paper, the equipment and method are verified using three test balls with different diameters and pasting different sizes of identification blocks at different positions on their surfaces. The experimental results show that the prediction accuracy R of the test set of the model is 0.9937, and the RMSE is 0.3391 cm2. It can be seen that the method has good prediction accuracy, which can provide a reference for the hyperspectral on‐line sorting method of external quality of spherical fruit.Practical applicationThis method provides an effective solution for the quality sorting production line of spherical fruits. In addition to agricultural product quality testing and food quality testing, similar to the detection of industrial products such as ball balls, the scheme provided in this manuscript can also be used as one of the options.The method proposed in this manuscript is suitable for all kinds of line scanning equipment, including hyperspectral imager and laser profilometer.
Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. Nevertheless, significant challenges persist in the practical application of this technology, particularly concerning adaptability, operational precision, and efficiency. This review seeks to systematically explore the advancements in unmanned agricultural operations, with a focus on onboard environmental sensing, full-coverage path planning, and autonomous operational control technologies. Additionally, this review discusses the challenges and future directions of key technologies for the autonomous operation of agricultural equipment in large fields. This review aspires to serve as a foundational reference for the development of autonomous operation technologies for large-scale agricultural equipment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.