SSM-Net for Plants Disease Identification in Low Data Regime

Jadon, Shruti

doi:10.1109/ai4g50087.2020.9311073

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…Argüeso et al (2020) used Siamese Network on the dataset PlantVillage (PV). Jadon (2020) proposed SSM-Net that uses the Siamese framework and combines two features from a Conv and a VGG16. Zhong et al (2020) proposed a novel generative model for zero-shot and few-shot recognition of citrus aurantium L. diseases by using conditional adversarial auto-encoders.…”

Section: Introductionmentioning

confidence: 99%

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition

et al. 2022

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Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application.

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Section: Introductionmentioning

confidence: 99%

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition

et al. 2022

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“…FSL was introduced very recently, and the results generated by this meta-learning approach are favorable. In the smart agriculture sector, FSL is widely used in plant disease identification (Wang and Wang, 2019;Li and Chao, 2021), plant counting (Karami et al, 2020), leaf classification (Afifi et al, 2020;Jadon, 2020;Tassis and Krohling, 2022) and fruit ripeness classification (Janarthan et al, 2020;Ng et al, 2022). The results for these experiments have exceeded 90%.…”

Section: Resultsmentioning

confidence: 99%

“…FSL has been proven in agriculture to be an effective method for plant disease identification (Argüeso et al, 2020). Recent FSL approaches in agriculture are mainly used for plant disease detection (Wang and Wang, 2019;Li and Chao, 2021), fruit detection and classification (Janarthan et al, 2020;Ng et al, 2022), leaf identification and classification (Afifi et al, 2020;Jadon, 2020;Tassis and Krohling, 2022) and pest detection Yang, 2020, 2021;Nuthalapati and Tunga, 2021) using the meta-train set which is subsequently used to build embeddings for the samples in the meta-test set.…”

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confidence: 99%

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Ragu

Teo²

2023

Front. Sustain. Food Syst.

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Smart agriculture is the application of modern information and communication technologies (ICT) to agriculture, leading to what we might call a third green revolution. These include object detection and classification such as plants, leaves, weeds, fruits as well as animals and pests in the agricultural domain. Object detection, one of the most fundamental and difficult issues in computer vision has attracted a lot of attention lately. Its evolution over the previous two decades can be seen as the pinnacle of computer vision advancement. The detection of objects can be done via digital image processing. Machine learning has achieved significant advances in the field of digital image processing in current years, significantly outperforming previous techniques. One of the techniques that is popular is Few-Shot Learning (FSL). FSL is a type of meta-learning in which a learner is given practice on several related tasks during the meta-training phase to be able to generalize successfully to new but related activities with a limited number of instances during the meta-testing phase. Here, the application of FSL in smart agriculture, with particular in the detection and classification is reported. The aim is to review the state of the art of currently available FSL models, networks, classifications, and offer some insights into possible future avenues of research. It is found that FSL shows a higher accuracy of 99.48% in vegetable disease recognition on a limited dataset. It is also shown that FSL is reliable to use with very few instances and less training time.

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“…The parameters of this filter are: wavelength of the sinusoidal factor ( ) ; standard deviation of the Gaussian envelope ( ) ; routing of the normal to the parallel stripes of a Gabor function ( ) ; phase offset ( ) ; and spatial aspect ratio ( ) . Equations ( 7) and (8) describe the transformation matrix that enables the detection of the distances extending in certain directions by the Gabor filter [18].…”

Section: Gabor Filtermentioning

confidence: 99%

Multi-channel capsule network ensemble for plant disease detection

Peker

2021

SN Appl. Sci.

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This study presents a new deep learning approach based on capsule networks and ensemble learning for the detection of plant diseases. The developed method is called as multi-channel capsule network ensemble. The main innovation behind the proposed method is the use of multi-channel capsule networks, individually trained on images applied different preprocessing techniques and then combined together. In this way, the final ensemble can better detect plant diseases by making use of different attributes of the data. Our experiments carried out using a well-known data set and various state-of-the-art classification methods demonstrate that our classification approach can provide competitive advantages in terms of classification accuracy. Article Highlights An ensemble of capsule networks has been developed for the automated detection of plant diseases with high accuracies. Better accuracy has been achieved with ensemble learning compared to a single model With the proposed method, better results have been obtained compared to state-of-the-art classification methods in the literature

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SSM-Net for Plants Disease Identification in Low Data Regime

Cited by 13 publications

References 11 publications

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition

Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition

Object detection and classification using few-shot learning in smart agriculture: A scoping mini review

Multi-channel capsule network ensemble for plant disease detection

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