Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Zhang, Zhenqian; Cao, Ruyue; Cheng, Peng; Liu, Renjie; Zhang, Man; Han, Ling

doi:10.3390/agronomy10040590

Cited by 17 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pixel counts of binary image representing the crop would not yield the spatial localisation of EOR within a given image, while it only generates an EOR trigger based on the image. In contrast, the EOR detection method proposed by authors of [19] uses Cr channel of Y CbCr colour space to calculate the position of the EOR within a given image. Such methods pose the advantage of early detection and potential failures due to noisy images in the method presented in [18].…”

Section: Related Workmentioning

confidence: 99%

Towards Infield Navigation: leveraging simulated data for crop row detection

Silva

Cielniak

Gao

2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

View full text Add to dashboard Cite

Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local feature matching based visual feedback to exit crop row. Depth images were used at crop row end to estimate the navigation distance within headland. The algorithm was tested on diverse headland areas with soil and vegetation. The proposed method could reach the end of the crop row and then navigate into the headland completely leaving behind the crop row with an error margin of 50 cm.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards Infield Navigation: leveraging simulated data for crop row detection

Silva

Cielniak

Gao

2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

View full text Add to dashboard Cite

show abstract

“…A classification accuracy of 82% is obtained for the harvesting and grading of lettuces. A pixel accumulation-based rice crop classification has been reported in [24]. A combination of two cameras was used for imaging and crop boundary estimation.…”

Section: Robotic Harvestingmentioning

confidence: 99%

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

2020

View full text Add to dashboard Cite

The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of fruit and vegetables have been obtained using a prototype designed to simulate the supermarket environment, including the lighting conditions. The weight information is used to change the coarse classification of 15 classes down to three, which are further used in AdaBoost-based Convolutional Neural Network (CNN) optimisation for fine classification. The training samples for each coarse class are weighted based on AdaBoost optimisation, which are updated on each iteration of a training phase. Multi-class likelihood distribution obtained by the fine classification stage is used to estimate a final classification with a softmax classifier. GoogleNet, MobileNet, and a custom CNN have been used for AdaBoost optimisation, with promising classification results.

show abstract

“…For instance, Wang et al (2015) employed the K-means color clustering algorithm to extract the target region of an apple, followed by utilizing the rotational inertia method based on the apple's good symmetry to extract its symmetry axis and determine the harvesting point. Additionally, Zhang et al (2010) drew test lines from various contour points of the strawberry's profile to its centroid and then utilized the mirror-image matching method to determine the strawberry's fruit axis, thus obtaining the harvesting points. However, for the forked carrot, which exhibits variable and irregular shapes, there is currently no research available on the determination of trimming paths.…”

Section: Introductionmentioning

confidence: 99%

Research on trimming path for forked carrots using contour‐based machine learning methods

Lv,

Zheng,

et al. 2024

J Food Process Engineering

View full text Add to dashboard Cite

Forked carrots are often used as animal feed or discarded directly, which affects the economic returns of growers and leads to resource waste and environmental pollution. After trimming, forked carrots become easier to peel and can be further processed. However, the lack of relevant studies and equipment hinders the full use of forked carrots, and the identification of fork points and determination of the trimming path are the main challenges in trimming forked carrots with unique and diverse shapes. Therefore, an automatic carrot‐trimming path recognition solution based on contour analysis and machine learning was proposed in this study to address the above challenges. Specifically, a cascaded model and a parallel model consisting of Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were constructed to identify fork points, and three trimming path determination methods based on fork points and carrot contours were proposed. The results demonstrated cascaded and parallel models achieved 100% and 92.7% recall rates, respectively, with accuracy rates of 90.4% and 100% and repetition rates of 97.1% and 96.4%. Among the trimming path determination methods, both the dynamic convex hull method and the static convex hull method achieved a convexity of 94.7%, surpassing 93.1% for the slope method. The static convex hull method exhibited the fastest speed in determining the trimming path, taking only 0.0032 s per carat. The parallel model and the static convex hull method could be effectively used for online determination of the trimming path for forked carrots.Practical applicationsTrimming forked carrots enhances usability, reduces resource wastage, and mitigates environmental pollution. Leveraging contour‐based machine learning algorithms, we achieved precise fork point recognition with broad applicability. Using fork point and carrot contour data, we determined trimming paths that render carrots convex for mechanical peeling. This approach contributes to advancing sustainable agriculture by optimizing resource utilization.

show abstract

Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Cited by 17 publications

References 16 publications

Towards Infield Navigation: leveraging simulated data for crop row detection

Towards Infield Navigation: leveraging simulated data for crop row detection

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

Research on trimming path for forked carrots using contour‐based machine learning methods

Contact Info

Product

Resources

About