Zero- and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders

Zhong, Fangming; Chen, Zhikui; Zhang, Yuchun; Xia, Feng

doi:10.1016/j.compag.2020.105828

Cited by 40 publications

(23 citation statements)

References 12 publications

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“…Experimental results showed that the FSL can obtain an average accuracy of 90% with only 60 training images, which was better than fine-grained transfer learning (73%). Zhong et al [99] proposed a generative model based on conditional adversarial auto-encoder (CAAE), which was used to perform generalized one-shot and few-shot learning in the case of few or even zero training samples to solve the problem of citrus diseases identification.…”

Section: Leaf-disease Detection Based On Small Samplesmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

412

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Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved. INDEX TERMSDeep learning, plant leaf disease detection, visualization, small sample, hyperspectral imaging.

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Section: Leaf-disease Detection Based On Small Samplesmentioning

confidence: 99%

Plant Disease Detection and Classification by Deep Learning—A Review

Zhang

2021

IEEE Access

412

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“…Agricultural scientists have proposed some other strategies, such as transfer learning (TL) [26] and few-shot learning (FSL) [27], to reduce the dependence of deep learning models on datasets. For example, TL has been successfully used for weed and disease identification [28][29][30], and FSL has been shown to be helpful for plant disease classification and recognition [31][32][33].…”

Section: Introductionmentioning

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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“…Li et al used the triplet loss to train feature extractor based on distance metric comparison and focused on combining few-shot algorithms and terminal realization [ 28 ]. Zhong et al used the conditional adversarial autoencoders to generate samples for the zero-shot and few-shot diseases recognition based on the visual and semantic features [ 29 ]. Li et al used the metric learning to analyze the single domain and cross domain of crop pests and plant diseases recognition [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised few-shot learning approach for plant diseases recognition

Chao

2021

Plant Methods

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Background Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality. The existing few-shot classification studies in agriculture are mainly based on supervised learning schemes, ignoring unlabeled data's helpful information. Methods In this paper, we proposed a semi-supervised few-shot learning approach to solve the plant leaf diseases recognition. Specifically, the public PlantVillage dataset is used and split into the source domain and target domain. Extensive comparison experiments considering the domain split and few-shot parameters (N-way, k-shot) were carried out to validate the correctness and generalization of proposed semi-supervised few-shot methods. In terms of selecting pseudo-labeled samples in the semi-supervised process, we adopted the confidence interval to determine the number of unlabeled samples for pseudo-labelling adaptively. Results The average improvement by the single semi-supervised method is 2.8%, and that by the iterative semi-supervised method is 4.6%. Conclusions The proposed methods can outperform other related works with fewer labeled training data.

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Zero- and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders

Cited by 40 publications

References 12 publications

Plant Disease Detection and Classification by Deep Learning—A Review

Plant Disease Detection and Classification by Deep Learning—A Review

A Review of Deep Learning in Multiscale Agricultural Sensing

Semi-supervised few-shot learning approach for plant diseases recognition

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