Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Yang, Jie; Chen, Yong; Yu, Jialin

doi:10.1002/ps.7979

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…Wang et al [22] aimed to improve the detection accuracy of nightshade seedlings with a YOLO-CBAM model, outperforming the original YOLOv5 with a precision of 94.65% and a recall of 90.17%. Yang et al [23] proposed a new neural network architecture to improve the accuracy of detecting broad-leaf weeds in alfalfa. 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.…”

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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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: 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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Evaluation of segmentation and semi‐supervised learning for spotted spurge recognition in bermudagrass turf

Joseph,

Gawron,

Zhao

et al. 2024

Intl Turfgrass Soc Res J

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Diverse datasets are crucial for training machine learning‐based weed recognition models. However, annotating (i.e., labeling) images can be laborious and time‐consuming. Choice of annotation method and training approach not only affects the overall model effectiveness but also its minimum training data requirements and development pace. Segmentation and semi‐supervised learning (SSL) may offer performance or training enhancements. This study evaluated (1) segmentation against object detection in spotted spurge [Chamaesyce maculata (L.) Small] recognition in Latitude 36 hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] maintained as a golf course fairway and (2) the potential of two‐step SSL‐based training procedure using data labeled both manually and automatically with a pre‐trained model to expedite the model development. The architecture used across this research was You Only Look Once version 8 (YOLOv8) employing nano, small, and medium variants. All models were trained with a dataset restricted to 1200 training and 300 validation images. Both annotation methods resulted in adequate spotted spurge identification, as evidenced with >0.60 (>0.50 acceptability threshold) medium average precision at intersection over union threshold of 0.50 (mAP@50). Although the difference was minimal, object detection performed superior to segmentation. The two‐step training effectively accelerated image annotation while preserving or improving (with 8:4 and 6:6 splits between manually and auto‐labeled data) object detection performance. Segmentation tolerated only 10:2 split and exhibited increased sensitivity to declining dataset quality proportionally to the increase in auto‐labeled images in the final training dataset. Findings show a two‐step SSL‐based training procedure expedites annotation, enhancing model development efficiency.

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Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches

Tian,

Zhao,

Yan

et al. 2024

Pest Management Science

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BACKGROUNDCrop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop‐specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets.RESULTSOur results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61–98.76%), Mixed (90.76–92.31%), and Field (68.56–73.81%). Although CSMs were found to reduce inter‐crop disease misclassifications, they also led to a slight increase in intra‐crop misclassifications.CONCLUSIONOur findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field‐specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry.

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Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

Cited by 4 publications

References 55 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

Evaluation of segmentation and semi‐supervised learning for spotted spurge recognition in bermudagrass turf

Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches

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