Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net

Sahin, Halil Mertkan; Miftahushudur, Tajul; Grieve, Bruce; Yin, Hujun

doi:10.1016/j.compag.2023.107956

Cited by 25 publications

(8 citation statements)

References 41 publications

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“…In comparison to traditional methods that utilize deep learning models to directly segment crops from weeds [20,24,25], the main advantages of the approach proposed in this paper are as follows:…”

Section: Image Segmentation Experiments and Analysismentioning

confidence: 99%

“…This architecture integrates the ResNet-101 backbone with image classification and segmentation modules and shows better detection performance for broad-leaf weeds in alfalfa compared to other models. Sahin et al [24] used multispectral imaging and a CRF-enhanced U-Net model to segment weeds and crops, achieving a mIoU of 88.3% on a sunflower dataset, providing a feasible method for early weed detection. Cui et al [25] proposed a semantic segmentation network, RDS Unet, based on corn seedling fields built upon an improved U-net network.…”

Section: Introductionmentioning

confidence: 99%

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An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Wang,

Yao,

et al. 2024

Agriculture

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In response to the limitations of existing methods in differentiating between vegetables and all types of weeds in farmlands, a new image segmentation method is proposed based on the improved YOLOv7-tiny. Building on the original YOLOv7-tiny framework, we replace the CIoU loss function with the WIoU loss function, substitute the Leaky ReLU loss function with the SiLU activation function, introduce the SimAM attention mechanism in the neck network, and integrate the PConv convolution module into the backbone network. The improved YOLOv7-tiny is used for vegetable target detection, while the ExG index, in combination with the OTSU method, is utilized to obtain a foreground image that includes both vegetables and weeds. By integrating the vegetable detection results with the foreground image, a vegetable distribution map is generated. Subsequently, by excluding the vegetable targets from the foreground image using the vegetable distribution map, a single weed target is obtained, thereby achieving accurate segmentation between vegetables and weeds. The experimental results show that the improved YOLOv7-tiny achieves an average precision of 96.5% for vegetable detection, with a frame rate of 89.3 fps, Params of 8.2 M, and FLOPs of 10.9 G, surpassing the original YOLOv7-tiny in both detection accuracy and speed. The image segmentation algorithm achieves a mIoU of 84.8% and an mPA of 97.8%. This method can effectively segment vegetables and a variety of weeds, reduce the complexity of segmentation with good feasibility, and provide a reference for the development of intelligent plant protection robots.

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Section: Image Segmentation Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Wang,

Yao,

et al. 2024

Agriculture

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“…As for the applications we are interested in this work, the answer is those that have at least one requirement for classification [ 12 ]. An example could be crop detection from satellite images [ 13 ], building segmentation in aerial photos [ 14 ], but also text translation [ 15 ]. Classification is also related to voice command recognition [ 16 ], speaker recognition [ 17 ], segmentation of the audio track according to speakers [ 18 ], recognition of speaker emotions with visual support [ 19 ], but also classification of objects of interest along with their localization in the image [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Cross Entropy in Deep Learning of Classifiers Is Unnecessary—ISBE Error Is All You Need

Skarbek

2024

Entropy

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In deep learning of classifiers, the cost function usually takes the form of a combination of SoftMax and CrossEntropy functions. The SoftMax unit transforms the scores predicted by the model network into assessments of the degree (probabilities) of an object’s membership to a given class. On the other hand, CrossEntropy measures the divergence of this prediction from the distribution of target scores. This work introduces the ISBE functionality, justifying the thesis about the redundancy of cross-entropy computation in deep learning of classifiers. Not only can we omit the calculation of entropy, but also, during back-propagation, there is no need to direct the error to the normalization unit for its backward transformation. Instead, the error is sent directly to the model’s network. Using examples of perceptron and convolutional networks as classifiers of images from the MNIST collection, it is observed for ISBE that results are not degraded with SoftMax only but also with other activation functions such as Sigmoid, Tanh, or their hard variants HardSigmoid and HardTanh. Moreover, savings in the total number of operations were observed within the forward and backward stages. The article is addressed to all deep learning enthusiasts but primarily to programmers and students interested in the design of deep models. For example, it illustrates in code snippets possible ways to implement ISBE functionality but also formally proves that the SoftMax trick only applies to the class of dilated SoftMax functions with relocations.

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“…The skip connections between corresponding layers in the contracting and expanding paths facilitate the propagation of fine-grained spatial information, enabling the network to retain detailed features from earlier layers while incorporating context from higher-level features. This unique architecture enables U-Net to effectively capture both local and global spatial dependencies, making it particularly suitable for extracting fine-grained spatial information from high-resolution multispectral imagery captured by UAVs [36].…”

Section: Introductionmentioning

confidence: 99%

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

Amarasingam,

Vanegas,

Hele

et al. 2024

Remote Sensing

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The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.

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Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net

Cited by 25 publications

References 41 publications

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields

Cross Entropy in Deep Learning of Classifiers Is Unnecessary—ISBE Error Is All You Need

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

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