CNN feature based graph convolutional network for weed and crop recognition in smart farming

Honghua, Jiang; Zhang, Chuanyin; Qiao, Yongliang; Zhang, Zhao; Zhang, Wenjing; Song, Changchun

doi:10.1016/j.compag.2020.105450

Cited by 182 publications

(84 citation statements)

References 19 publications

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“…The evaluation on the DeepWeeds dataset reached the highest accuracy of 98.1% at the time. Jiang et al [ 144 ] proposed semi-supervised GCN–ResNet101 to improve the recognition accuracy of crops and weeds in a limited labeled dataset, combining the advantages of CNN features and the semi-supervised learning capability of the graph. Tang et al [ 145 ] combined k-means unsupervised feature learning with the advantages of multilayered and refined CNN parameters as a pretraining process for the identification of weeds in soybean seedlings.…”

Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

172

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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Section: Weed Detection and Identification Methods Based On Deep Learningmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

172

View full text Add to dashboard Cite

show abstract

“…Over the past decades, researchers have used computer vision technology in agriculture for estimating crop yields (Gong et al, 2013 ; Deng et al, 2020 ), detecting crop nutritional deficiencies (Xu et al, 2011 ; Baresel et al, 2017 ; Tao et al, 2020 ), estimating geometric sizes of crop (Liu et al, 2019 ), and recognizing weeds (Jiang et al, 2020 ). Several different approaches of computer vision have also been used for the diagnosis of crop diseases, such as image processing, pattern recognition, support vector machine, and hyperspectral detection (Ngugi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Rice Diseases Using Deep Learning

et al. 2021

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Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.

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“…Conventionally human craft features such as color, shape, and texture are used for earlystage detection [20], but they are prone to errors. They have led to the introduction of deep learning in weed classification and recognition [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A novel semi-supervised framework for UAV based crop/weed classification

et al. 2021

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Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agriculture (PA). In this regard, targeted weed control will be a critical component significantly helping in achieving the goal. A prerequisite for such control is a robust classification system that could accurately identify weed crops in a field. In this regard, Unmanned Aerial Vehicles (UAVs) can acquire high-resolution images providing detailed information for the distribution of weeds and offers a cost-efficient solution. Most of the established classification systems deploying UAV imagery are supervised, relying on image labels. However, this is a time-consuming and tedious task. In this study, the development of an optimized semi-supervised learning approach is proposed, offering a semi-supervised generative adversarial network for crops and weeds classification at early growth stage. The proposed algorithm consists of a generator that provides extra training data for the discriminator, which distinguishes weeds and crops using a small number of image labels. The proposed system was evaluated extensively on the Red Green Blue (RGB) images obtained by a quadcopter in two different croplands (pea and strawberry). The method achieved an average accuracy of 90% when 80% of training data was unlabeled. The proposed system was compared with several standards supervised learning classifiers and the results demonstrated that this technique could be applied for challenging tasks of crops and weeds classification, mainly when the labeled samples are small at less training time.

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CNN feature based graph convolutional network for weed and crop recognition in smart farming

Cited by 182 publications

References 19 publications

Review of Weed Detection Methods Based on Computer Vision

Review of Weed Detection Methods Based on Computer Vision

Automatic Diagnosis of Rice Diseases Using Deep Learning

A novel semi-supervised framework for UAV based crop/weed classification

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