Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain

Giakoumoglou, Nikolaos; Pechlivani, Eleftheria Maria; Tzovaras, Dimitrios

doi:10.1016/j.atech.2023.100258

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Pechlivani et al (2023) [19] developed a cost-effective hyperspectral camera using off-theshelf components and open-source software, enabling the simplified capture and analysis of hyperspectral imaging data. Giakoumoglou et al (2023) [32] utilized multispectral imaging to identify the plant disease, grey mold, applying DL models that achieved a 93% accuracy in classification and a 0.88 mean Average Precision (mAP) in detection. Georgantopoulos et al (2023) [33] provided a dataset of multispectral images for tomato plants afflicted with T. absoluta and Leveillula taurica, using a Faster-RCNN model to obtain a 90% F1 score and a 20.2% mAP for detecting and classifying lesions.…”

Section: Spectral Imaging In Agriculturementioning

confidence: 99%

“…The application of AI techniques in agriculture is transforming the landscape of farming practices, as reviewed in a comprehensive survey [39]. Giakoumoglou et al (2023) [40] developed a method for generating synthetic datasets for object detection using Denoising Diffusion Probabilistic Models (DDPMs), showcasing its effectiveness in agricultural pest detection with a 0.66 mAP using YOLOv8. Giakoumoglou et al (2022) [41] used YOLOv3, YOLOv5, Faster R-CNN, Mask R-CNN, and RetinaNet to detect whiteflies and black aphids, achieving an mAP of 75%.…”

Section: Artificial Intelligence In Agriculturementioning

confidence: 99%

“…Rubanga et al (2020) [48] evaluated the severity of T. absoluta damage on tomato plants, employing DL architectures that achieved an average accuracy of 87.2%. Lastly, Giakoumoglou et al (2023) [49] combined DL models, including Faster R-CNN and RetinaNet, with ensemble techniques, significantly enhancing the detection of T. absoluta in tomatoes, as demonstrated by a 20% increase in mean Average Precision scores using real-life field and greenhouse data.…”

Section: Artificial Intelligence In Agriculturementioning

confidence: 99%

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A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

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Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

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Section: Spectral Imaging In Agriculturementioning

confidence: 99%

Section: Artificial Intelligence In Agriculturementioning

confidence: 99%

Section: Artificial Intelligence In Agriculturementioning

confidence: 99%

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A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

Self Cite

View full text Add to dashboard Cite

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“…Significant progress has been made in agricultural applications by employing deep learning methods. In particular, the identification of diverse plant diseases and pests has yielded promising and impressive outcomes, as evidenced by recent studies [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

Giakoumoglou,

Pechlivani,

Frangakis

et al. 2023

Self Cite

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Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. Additionally, this study highlights the importance of utilizing a dataset captured in real settings in open-field and greenhouse environments to address the complexity of real-life challenges in object detection of plant health scenarios. The effectiveness of deep-learning-based models, including Faster R-CNN and RetinaNet, was evaluated in terms of detecting T. absoluta damage. The initial model evaluations revealed diminishing performance levels across various model configurations, including different backbones and heads. To enhance detection predictions and improve mean Average Precision (mAP) scores, ensemble techniques were applied such as Non-Maximum Suppression (NMS), Soft Non-Maximum Suppression (Soft NMS), Non-Maximum Weighted (NMW), and Weighted Boxes Fusion (WBF). The outcomes shown that the WBF technique significantly improved the mAP scores, resulting in a 20% improvement from 0.58 (max mAP from individual models) to 0.70. The results of this study contribute to the field of agricultural pest detection by emphasizing the potential of deep learning and ensemble techniques in improving the accuracy and reliability of object detection models.

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“…High-precision computer vision technology stands as the pivotal impetus propelling industrial intelligence to the forefront. In the sphere of agricultural production, concomitant with the maturation of artificial intelligence and deep learning technology, the purview of intelligent farm management, the monitoring of crop diseases through the prism of computer vision, the surveillance of ripening stages, and the stringent quality control have emerged as focal points, catalyzing an upsurge in agricultural production efficiency ( Giakoumoglou, Pechlivani & Tzovaras, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Design of smart citrus picking model based on Mask RCNN and adaptive threshold segmentation

Guo,

Shi,

Ahmad

2024

PeerJ Computer Science

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Smart agriculture is steadily progressing towards automation and heightened efficacy. The rapid ascent of deep learning technology provides a robust foundation for this trajectory. Leveraging computer vision and the depths of deep learning techniques enables real-time monitoring and management within agriculture, facilitating swift detection of plant growth and autonomous assessment of ripeness. In response to the demands of smart agriculture, this exposition delves into automated citrus harvesting, presenting an ATT-MRCNN target detection model that seamlessly integrates channel attention and spatial attention mechanisms for discerning and identifying citrus images. This framework commences by subjecting diverse citrus image classifications to Mask Region-based CNN’s (Mask RCNN’s) discerning scrutiny, enhancing the model’s efficacy through the incorporation of attention mechanisms. During the model’s training phase, transfer learning is utilized to expand data performance and optimize training efficiency, culminating in parameter initialization. Empirical results notably demonstrate that this method achieves a recognition rate surpassing the 95% threshold across the three sensory recognition tasks. This provides invaluable algorithmic support and essential guidance for the imminent era of intelligent harvesting.

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Generate-Paste-Blend-Detect: Synthetic dataset for object detection in the agriculture domain

Cited by 12 publications

References 23 publications

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

Design of smart citrus picking model based on Mask RCNN and adaptive threshold segmentation

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