FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications

Kamal, Shoaib; Shende, Vaishali Gajendra; Swaroopa, Korla; Madhavi, P. Bindhu; Akram, P. Saleem; Pant, Kumud; Patil, Shantala Devi; Sahile, Kibebe

doi:10.1155/2022/2770706

Cited by 18 publications

(4 citation statements)

References 21 publications

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“…The key task for successful weed detection is efficient vegetation segmentation. 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.…”

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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“…On the basis of this approach, Kamal et al. [ 28 ] accomplished weed-crop segmentation. The processing results for FCN are not fine enough, and the relationship between the pixels is not taken into account.…”

Section: Introductionmentioning

confidence: 99%

Improved Transformer for Time Series Senescence Root Recognition

Tang,

Cheng,

et al. 2024

Plant Phenomics

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The root is an important organ for plants to obtain nutrients and water, and its phenotypic characteristics are closely related to its functions. Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published. In light of this, this paper suggests a technique based on the transformer neural network for retrieving cotton’s in situ root senescence properties. High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation. By comparing the semantic segmentation of the root system by general convolutional neural networks and transformer neural networks, SegFormer-UN (large) achieves the optimal evaluation metrics with mIoU, mRecall, mPrecision, and mF1 metric values of 81.52%, 86.87%, 90.98%, and 88.81%, respectively. The segmentation results indicate more accurate predictions at the connections of root systems in the segmented images. In contrast to 2 algorithms for cotton root senescence extraction based on deep learning and image processing, the in situ root senescence recognition algorithm using the SegFormer-UN model has a parameter count of 5.81 million and operates at a fast speed, approximately 4 min per image. It can accurately identify senescence roots in the image. We propose that the SegFormer-UN model can rapidly and nondestructively identify senescence root in in situ root images, providing important methodological support for efficient crop senescence research.

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“…(2021) used a multiscale attention semantic segmentation method to automatically detect farmland anomalies. Kamal et al. (2022) used the fully convolutional network (FCN) and ResNet to analyse the accuracy of the weed and crop segment.…”

Section: Introductionmentioning

confidence: 99%

“…Anand et al (2021) used a multiscale attention semantic segmentation method to automatically detect farmland anomalies. Kamal et al (2022) used the fully convolutional network (FCN) and ResNet to analyse the accuracy of the weed and crop segment. A global accuracy of more than 90% in the verification package was achieved for both structures, which verified that FCN network can assist in agricultural weed and crop segmentation.…”

Section: Introductionmentioning

confidence: 99%

An efficient approach to detect and track winter flush growth of litchi tree based on UAV remote sensing and semantic segmentation

Bai,

Liang,

Long

et al. 2023

Front. Plant Sci.

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The immature winter flush affects the flower bud differentiation, flowering and fruit of litchi, and then seriously reduces the yield of litchi. However, at present, the area estimation and growth process monitoring of winter flush still rely on manual judgment and operation, so it is impossible to accurately and effectively control flush. An efficient approach is proposed in this paper to detect the litchi flush from the unmanned aerial vehicle (UAV) remoting images of litchi crown and track winter flush growth of litchi tree. The proposed model is constructed based on U-Net network, of which the encoder is replaced by MobeilNetV3 backbone network to reduce model parameters and computation. Moreover, Convolutional Block Attention Module (CBAM) is integrated and convolutional layer is added to enhance feature extraction ability, and transfer learning is adopted to solve the problem of small data volume. As a result, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) on the flush dataset are increased from 90.95% and 83.3% to 93.4% and 85%, respectively. Moreover, the size of the proposed model is reduced by 15% from the original model. In addition, the segmentation model is applied to the tracking of winter flushes on the canopy of litchi trees and investigating the two growth processes of litchi flushes (late-autumn shoots growing into flushes and flushes growing into mature leaves). It is revealed that the growth processes of flushes in a particular branch region can be quantitatively analysed based on the UAV images and the proposed semantic segmentation model. The results also demonstrate that a sudden drop in temperature can promote the rapid transformation of late-autumn shoots into flushes. The method proposed in this paper provide a new technique for accurate management of litchi flush and a possibility for the area estimation and growth process monitoring of winter flush, which can assist in the control operation and yield prediction of litchi orchards.

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FCN Network-Based Weed and Crop Segmentation for IoT-Aided Agriculture Applications

Cited by 18 publications

References 21 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

Improved Transformer for Time Series Senescence Root Recognition

An efficient approach to detect and track winter flush growth of litchi tree based on UAV remote sensing and semantic segmentation

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