Deep pruned nets for efficient image-based plants disease classification

Too, Edna C.; Li, Yujian; Kwao, Pius; Njuki, Sam; Mosomi, Mugendi E.; Kibet, Julius

doi:10.3233/jifs-190184

Cited by 15 publications

(2 citation statements)

References 15 publications

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“…In agriculture, deep learning-based automatic classification of several categories is a hot topic of research (Too et al, 2019;. Yet, in general, people still lean on non-automated classification by experts for the classification of distinct species.…”

Section: Limitations Of Deep Learningmentioning

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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Section: Limitations Of Deep Learningmentioning

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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“…The other way is to solve the classification problem with few data, also called few-shot learning, which is more suitable for practical applications. For example, some other works focused on model compression by pruning [ 23 ], shallow model [ 24 ], and lightweight network [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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

Chao

2021

Plant Methods

100

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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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Plant Biotic Disease Identification and Classification Based on Leaf Image: A Review

Kaur

Gautam

2021

Lecture Notes in Networks and Systems

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Deep pruned nets for efficient image-based plants disease classification

Cited by 15 publications

References 15 publications

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

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

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

Plant Biotic Disease Identification and Classification Based on Leaf Image: A Review

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