Tomato leaf segmentation algorithms for mobile phone applications using deep learning

Ngugi, Lawrence C.; Abdelwahab, Moataz M.; Abo‐Zahhad, Mohammed

doi:10.1016/j.compag.2020.105788

Cited by 88 publications

(48 citation statements)

References 64 publications

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“…A subset of this dataset contains nine tomato leaf diseases and one healthy class that has been utilized by most of the recent deep learning-based works on tomato leaf disease classification. Several works on tomato leaf diseases also focused on segmenting leaves from complex backgrounds [32], real-time localization of diseases [33]- [35], detection of leaf disease in early-stage [36], visualizing the learned features of different layers of CNN model [37], [38], combining leaf segmentation and classification [39], and so on. These works mostly targeted removing the restrictions of lighting conditions and uniformity of complex backgrounds.…”

Section: Related Workmentioning

confidence: 99%

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

et al. 2022

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To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for low-end devices. Our codes and models will be made available upon publication.

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Section: Related Workmentioning

confidence: 99%

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

et al. 2022

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show abstract

“…A subset of this dataset contains nine tomato leaf diseases and one healthy class that has been utilized by most of the recent deep learningbased works on tomato leaf disease classification. Several works on tomato leaf diseases also focused on segmenting leaves from complex backgrounds (Ngugi et al, 2020), real-time localization of diseases (Liu & Wang, 2020b;Zhang et al, 2020;Fuentes et al, 2017b), detection of leaf disease in early-stage (Liu & Wang, 2020a), visualizing the learned features of different layers of CNN model (Brahimi et al, 2017;Fuentes et al, 2017a) and so on. These works mostly targeted removing the restrictions of lighting conditions and uniformity of complex backgrounds.…”

Section: Related Workmentioning

confidence: 99%

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

Ahmed,

Hasan,

Ahmed

et al. 2021

Preprint

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To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for real-life applications in low-end devices. Our codes and models will be made available upon publication.

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“…The detection results not only serve as a guide for the manipulator to harvest chrysanthemum in the subsequent operation, but also determine the detection accuracy in chrysanthemum harvesting. Although in recent years, methods based on deep convolutional neural networks (CNNs) have made remarkable achievements in object detection tasks [4][5][6][7][8][9][10], under agricultural application scenarios, it is still difficult to build a lightweight network for a selective harvesting robot that can adapt to complex unstructured scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The task of harvesting chrysanthemum at a specific maturity stage usually requires shortening the reasoning time on small devices, which poses a serious challenge to computer vision algorithms. Although some methods are specially designed for mobile CPUs [4,29,30], the depth-wise separable convolution techniques adopted by these methods are not compatible with industrial integrated circuit (IC) design, examples of which include application-specific integrated circuits (ASICs) and edge computing systems. In view of this, a lightweight network based on feature fusion is proposed in this paper, which can be deployed on a mobile GPU [31] without compromising performance.…”

Section: Introductionmentioning

confidence: 99%

Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model

Nyalala

Chen

2021

Agronomy

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Detecting the flowering stage of tea chrysanthemum is a key mechanism of the selective chrysanthemum harvesting robot. However, under complex, unstructured scenarios, such as illumination variation, occlusion, and overlapping, detecting tea chrysanthemum at a specific flowering stage is a real challenge. This paper proposes a highly fused, lightweight detection model named the Fusion-YOLO (F-YOLO) model. First, cutout and mosaic input components are equipped, with which the fusion module can better understand the features of the chrysanthemum through slicing. In the backbone component, the Cross-Stage Partial DenseNet (CSPDenseNet) network is used as the main network, and feature fusion modules are added to maximize the gradient flow difference. Next, in the neck component, the Cross-Stage Partial ResNeXt (CSPResNeXt) network is taken as the main network to truncate the redundant gradient flow. Finally, in the head component, the multi-scale fusion network is adopted to aggregate the parameters of two different detection layers from different backbone layers. The results show that the F-YOLO model is superior to state-of-the-art technologies in terms of object detection, that this method can be deployed on a single mobile GPU, and that it will be one of key technologies to build a selective chrysanthemum harvesting robot system in the future.

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Tomato leaf segmentation algorithms for mobile phone applications using deep learning

Cited by 88 publications

References 64 publications

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification

Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model

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