Corrigendum to: ‘Identification of plant leaf diseases using a nine-layer deep convolutional neural network’, Computers &amp; Electrical Engineering Journal at Volume 76, June 2019, Pages 323--338

Geetharamani, G.; Pandian, J. Arun

doi:10.1016/j.compeleceng.2019.08.010

Cited by 103 publications

(133 citation statements)

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“…It appeared that increasing the training epochs and applying a self-built model were benecial for identifying vigor. A similar conclusion was reached by Geetharamani et al (2019) in their study to identify plant leaf diseases. 35 As shown in Table 1, over 94% accuracy in identifying vigor was achieved using the spectral images combined with deep learning algorithms.…”

Section: Seed Vigor Identicationsupporting

confidence: 81%

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Yang

Chen

et al. 2020

RSC Adv.

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Section: Seed Vigor Identicationsupporting

confidence: 81%

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Yang

Chen

et al. 2020

RSC Adv.

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“…For the comparative analysis, we used principal component analysis for dimensionality reduction. It is a well-known synthetic data augmentation technique first used in AlexNet ( Krizhevsky et al., 2012 ) and later ( Geetharmani and Pandian, 2019 ; Tongcham et al., 2020 ) considered for plant leaf disease detection. In this experimental setup, we used a different number of components for dimensionality reduction.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Nitin

Gupta²,

Yadav³

et al. 2023

Front. Plant Sci.

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Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.

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“…For the task of identifying vegetable diseases, most of the current research is based on the PlantVillage dataset (Geetharamani & Pandian, 2019). This dataset covers disease images of a wide range of common vegetables, such as tomatoes, peppers, and potatoes, which is helpful for studying the phenotypes of different types of diseases on the same crop.…”

Section: Applications Of Meta‐learning In Plant Disease Recognitionmentioning

confidence: 99%

Meta‐learning shows great potential in plant disease recognition under few available samples

Deng

Wang

et al. 2023

The Plant Journal

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Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, metalearning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta-learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta-learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta-learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples.

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Corrigendum to: ‘Identification of plant leaf diseases using a nine-layer deep convolutional neural network’, Computers & Electrical Engineering Journal at Volume 76, June 2019, Pages 323--338

Cited by 103 publications

References 0 publications

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

Meta‐learning shows great potential in plant disease recognition under few available samples

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