DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage

Das, Mohana; Bais, Abdul

doi:10.1109/access.2021.3108003

Cited by 17 publications

(6 citation statements)

References 45 publications

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“…Kamal et al (2022) evaluated deep machine learning algorithms for differentiating weeds from crop plants, utilizing an open carrot field image database. Das and Bais (2021) introduced DeepVeg, which focuses on the smallest (damage) class without impacting other classes to address the problem of class imbalance. Mishra et al (2022) proposed an Inception V4 architecture approach based on deep convolutional neural networks, using RGB weed and crop images.…”

Section: Weed Identificationmentioning

confidence: 99%

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Lei,

Yang,

Yang

et al. 2024

Artif Intell Rev

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Image segmentation is a crucial task in computer vision, which divides a digital image into multiple segments and objects. In agriculture, image segmentation is extensively used for crop and soil monitoring, predicting the best times to sow, fertilize, and harvest, estimating crop yield, and detecting plant diseases. However, image segmentation faces difficulties in agriculture, such as the challenges of disease staging recognition, labeling inconsistency, and changes in plant morphology with the environment. Consequently, we have conducted a comprehensive review of image segmentation techniques based on deep learning, exploring the development and prospects of image segmentation in agriculture. Deep learning-based image segmentation solutions widely used in agriculture are categorized into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks, graph neural networks, instance segmentation networks, and transformer-based models. In addition, the applications of image segmentation methods in agriculture are presented, such as plant disease detection, weed identification, crop growth monitoring, crop yield estimation, and counting. Furthermore, a collection of publicly available plant image segmentation datasets has been reviewed, and the evaluation and comparison of performance for image segmentation algorithms have been conducted on benchmark datasets. Finally, there is a discussion of the challenges and future prospects of image segmentation in agriculture.

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Section: Weed Identificationmentioning

confidence: 99%

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Lei,

Yang,

Yang

et al. 2024

Artif Intell Rev

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“…However, these methods rely heavily on handcrafted features, which are domain specific and lack generality. Since the concept of deep learning was introduced by Hinton [8], convolutional neural networks (CNNs) have shown strong feature extraction capabilities in image classification [9][10][11], detection [12][13][14] and segmentation [15][16][17], and have found extensive applications in agriculture [18][19][20]. The authors of [21] presented a deep learning segmentation model that is able to distinguish between different plant species at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

IPMCNet: A Lightweight Algorithm for Invasive Plant Multiclassification

Chen,

Qiao,

Qin

et al. 2024

Agronomy

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Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model’s parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model’s accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy.

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“…Das and Bais. [35] constructed a novel network, named DeepVeg, in which the pyramid pooling module was introduced into the encoder and decoder network to extract multi-level resolution features. The MIOU and accuracy of the DeepVeg model are 0.76 and 0.97, respectively.…”

Section: Introductionmentioning

confidence: 99%

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

Sun,

Zhang,

Zhou

et al. 2024

IEEE Access

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Automated weeding equipment is urgently needed to deal with weeds in farmlands in the context of the rapid development of Intelligent Agriculture. A system for accurately identifying crops and weeds in images is crucial component of automated weeding equipment. However, in the field environment, crops and weeds grow intertwined, and weeds are very similar to sugarcane leaves, making it difficult to accurately segment crops, weeds, and their boundaries from images. In this paper, we proposed a novel network that fully utilizes low-level semantic information to accurately segment crops and weeds in images, improving the accuracy of crop and weed segmentation while reducing the need for training weight parameters and improving speed in the prediction stage. Specifically, we made three important modifications for crop and weed identification. First, a Multi-scale Feature Extraction and Fusion module (MFEF) was designed to capture abundant low-level semantic feature information. Afterward, we introduce a Global Response Normalization (GRN) block to select more useful feature information. Finally, a series of residual attention transformer layers are designed to transmit the long-range dependency information extracted between layers. Numerous experimental results confirmed that our proposed network achieved excellent performance in segmenting sugarcane and weed images. Specifically, (1) the mean accuracy and, Mean Intersection of Union (MIoU) reached 96.97% and 94.13%, respectively, (2) the training parameters of the model have been reduced by more than 25%, improving the Frames Per Second (FPS) value of the prediction process, and (3) is also effective on the publicly available BoniRob Dataset, indicating that the proposed model has considerable generalization ability. This study provides an accurate weed identification map and has reference significance for subsequent systems as mechanical weeding equipment.

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DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage

Cited by 17 publications

References 45 publications

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

IPMCNet: A Lightweight Algorithm for Invasive Plant Multiclassification

An Improved Transformer Network With Multi-Scale Convolution for Weed Identification in Sugarcane Field

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