Unsupervised deep learning and semi-automatic data labeling in weed discrimination

Ferreira, Alessandro dos Santos; Freitas, Daniel Matte; Silva, Gercina Gonçalves da; Pistori, Hemerson; Folhes, Marcelo Theophilo

doi:10.1016/j.compag.2019.104963

Cited by 62 publications

(15 citation statements)

References 21 publications

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“…The abovementioned architectures can be classified into supervised deep neural networks, as they rely on time-consuming manual data labeling. In addition, dos Santos Ferreira, A. et al (2019) were interested in unsupervised deep learning applications for weed discrimination [71]. With the above two datasets (Grass-Broadleaf and DeepWeeds), they evaluated the performance of two unsupervised deep clustering algorithms and proposed an approach where semi-automatic data labeling was used to generated annotations.…”

Section: Canopy Scale: Plant and Weed Classification Identification A...mentioning

confidence: 99%

A Review of Deep Learning in Multiscale Agricultural Sensing

et al. 2022

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Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.

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Section: Canopy Scale: Plant and Weed Classification Identification A...mentioning

confidence: 99%

A Review of Deep Learning in Multiscale Agricultural Sensing

et al. 2022

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“…In addition, low tolerance between the cutting point and the crop location requires an accurate weed classification against the main crop. Therefore, several works have been conducted in the context of remote sensing image processing to detect and improve site-specific management [69][70][71].…”

Section: Image Classification and Validationmentioning

confidence: 99%

“…This proposed method can be the best option, since supervised labelling is expensive and challenging and requires human expertise. Dos Santos Ferreira et al [71] evaluated the unsupervised deep learning performance to discriminate weeds from soybean in UAV images. They tested two unsupervised deep clustering algorithms, joint unsupervised learning of deep representations and image clusters (JULE) and deep clustering for unsupervised learning of visual features (DeepCluster), using two public weed datasets.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

et al. 2021

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This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

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“…Deep unsupervised learning was explored in a recent weed study [72], which explored two methods: joint unsupervised learning of deep representations and image clusters (JULE) and deep clustering for unsupervised learning of visual features (DeepCluster). They adopted the CNN outputs as features for a clustering algorithm and specified pseudo labels for samples based on the clustering results.…”

Section: Weed Image Classificationmentioning

confidence: 99%

Deep Learning Techniques for In-Crop Weed Identification: A Review

Hu,

Wang,

Coleman

et al. 2021

Preprint

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Weeds are a significant threat to the agricultural productivity and the environment. The increasing demand for sustainable agriculture has driven innovations in accurate weed control technologies aimed at reducing the reliance on herbicides. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent progresses on deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem.

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Unsupervised deep learning and semi-automatic data labeling in weed discrimination

Cited by 62 publications

References 21 publications

A Review of Deep Learning in Multiscale Agricultural Sensing

A Review of Deep Learning in Multiscale Agricultural Sensing

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

Deep Learning Techniques for In-Crop Weed Identification: A Review

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