&amp;lt;i&amp;gt;Development of a Machine Learning-based Assistance System for Computer-Aided Process Optimization within a Self-Propelled Sugar Beet Harvester&amp;lt;/i&amp;gt;

Schwich, Steffen; Schattenberg, Jan; Frerichs, Ludger

doi:10.13031/aim.202000952

Cited by 3 publications

(4 citation statements)

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“…Furthermore, low resolution of the image, due to dry soil covering the camera lens during the harvesting, impacts on the performance of the developed models impacts, and the models are not able to extract enough features to perform accurate detection. Although the cleaning units were covered by a black tarpaulin sheet, the images were affected by sunlight during sunset and/or sunrise (Figure 9B), which has already been reported by [17] as an important parameter negatively affecting the quality of images in sugar beet harvesters. Furthermore, in this study the testing time of the detection models was computed and is shown in Figure 10.…”

Section: Resultsmentioning

confidence: 56%

“…However, no studies have yet been reported for real-time detection of sugar beet mechanical damage during harvesting. Addressing the challenge of real-time monitoring of damages in harvesters is a key parameter in introducing advanced models into the design of automatic and optimized control system to improve the quality of the sugar beet during harvesting [17]. Hence, the aim of this study was to develop various CNN vision models, i.e., YOLO v4, Faster R-CNN and R-FCN for detection of sugar beet damage during harvesting using digital cameras installed in a sugar beet harvester.…”

Section: Introductionmentioning

confidence: 99%

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Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

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Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

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Section: Resultsmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

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“…In particular, this is due to improper adjustment of the beet head cutter, incorrect adjustment of the depth and distance between the openers of the root crop grubber, incorrect displacement of the harvesting unit relative to the beet rows, the incorrectly selected rotation speed of the separating complex, conveyor, elevator, etc. [5,6]. A significant part of the losses can be avoided by installing adaptive devices based on computer vision systems.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

et al. 2022

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Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu's binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 × 416 pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which ranged from 97% to 100%, depending on the damage class. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy ranged from 90% to 95% depending on the root crops damage class.

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“…The computer-aided design approach merged mathematical algorithms and computational models to study complex system behaviors [15][16][17]. New strategies have been introduced where field data and simulation models are synergistically combined, an overall framework referred to as model-based system testing [18,19]. In 2017, Golpira and Golpira [20] merged conventional design methodologies with metaheuristic modeling combining genetic algorithms with fuzzy logic to develop a hybrid computational algorithm that predicted the optimal sizing of a grain harvester platform.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Golpira

Sola-Guirado

2022

Agriculture

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Conventional redesign methodologies applied on the grain harvester headers for the mechanical harvesting of chickpeas cause its progress to not be as rapid and technological. This paper presents a hybrid modeling-optimization methodology to design harvester reels for efficient chickpea harvesting. The five fabricated headers were tested in both real and virtual modeling environments to optimize the operational parameters of the reel for minimum losses. Harvesting losses data gathered from chickpea fields over ten years of trials were fed into a fuzzy logic model, which in turn was merged with simulated annealing to develop a simulator. To this end, simulated annealing was used to produce combinations of reel diameter and number of bats, to be fed into the fuzzy model until achieving a minimum harvesting loss. The proposed model predicts the reel structure measured in-field evaluation, which fits well with the previously established mathematical model. A significant improvement in harvesting performance, 71% pod harvesting, validates the benefits of the proposed fuzzy-simulated annealing approach to optimize the design of grain harvester headers.

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<i>Development of a Machine Learning-based Assistance System for Computer-Aided Process Optimization within a Self-Propelled Sugar Beet Harvester</i>

Cited by 3 publications

References 0 publications

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

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