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

Nasirahmadi, Abozar; Wilczek, Ulrike; Hensel, Oliver

doi:10.3390/agriculture11111111

Cited by 18 publications

(11 citation statements)

References 33 publications

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“…They obtained better computational cost and speed with YOLOv4-tiny, concluding that the best model is the YOLOv4 in accuracy and speed. In [13], the authors properly evaluate various detector models based on the CNN, such as YOLO v4, region-based fully convolutional network (R-FCN) and Faster R-CNN, in order to detect visible mechanical damage in sugar beet during harvesting in a harvester machine from RGB videos images. The better experimental results showed a recall, precision and F1-score of about 92, 94 and 93% respectively, and higher speed of around 29 frames per second.…”

Section: Scientific Publicationsmentioning

confidence: 99%

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Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

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Section: Scientific Publicationsmentioning

confidence: 99%

“…Parico et al [12] RGB video YOLOv4, YOLOv4-CSP, YOLOv4-tiny. Nasirahmadi et al [13] RGB video YOLO v4, R-FCN and Faster R-CNN Wang et al [14] RGB video YOLOv3…”

Section: Article Data Type Modelmentioning

confidence: 99%

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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“…Maximum accuracy of 91.37 percent is reached when evaluating the accuracies of diverse training and testing datasets. In the case of sugar beet, an existing model is upgraded, utilizing the faster region-based CNN architecture by modifying the parameters for recognizing disease-affected regions ( Nasirahmadi et al., 2021 ). The dataset comprises 155 photos of sugar beets, and the proposed framework attained an accuracy rate of 95.48 percent.…”

Section: Related Workmentioning

confidence: 99%

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Shoaib

Shah²,

Hussain³

et al. 2022

Front. Plant Sci.

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Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield.

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“…The AI and big data support better and precise farm monitoring, data acquisition and analytics, improve information extraction from sensors as well as farm management [11]. For instance, crop health and productivity can be monitored and controlled using advanced AI and deep learning techniques [12]. Datadriven approaches augment on-farm decision-making capabilities, improve crop yield, reduce losses, and therefore, benefit farmers.…”

Section: Introductionmentioning

confidence: 99%

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Nasirahmadi

Hensel

2022

Sensors

Self Cite

137

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Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

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

Cited by 18 publications

References 33 publications

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

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