The Impact of Rice Diseases in Tropical Asia

Castilla, N. P.; Macasero, John Bethany M.; Villa, Joselito E.; Sparks, Adam H.; Willocquet, Laetitia; Savary, Serge

doi:10.1007/978-3-030-57899-2_6

Cited by 2 publications

(1 citation statement)

References 56 publications

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“…Rice is the staple food of more than 65% of the Chinese people (Deng et al, 2019). However, pests and diseases always accompany the process of rice planting and production (Laha et al, 2017;Castilla et al, 2021). The prevention and control of rice pests and diseases could be greatly improved through their accurate classification.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

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The accurate classification of crop pests and diseases is essential for their prevention and control. However, datasets of pest and disease images collected in the field usually exhibit long-tailed distributions with heavy category imbalance, posing great challenges for a deep recognition and classification model. This paper proposes a novel convolutional rebalancing network to classify rice pests and diseases from image datasets collected in the field. To improve the classification performance, the proposed network includes a convolutional rebalancing module, an image augmentation module, and a feature fusion module. In the convolutional rebalancing module, instance-balanced sampling is used to extract features of the images in the rice pest and disease dataset, while reversed sampling is used to improve feature extraction of the categories with fewer images in the dataset. Building on the convolutional rebalancing module, we design an image augmentation module to augment the training data effectively. To further enhance the classification performance, a feature fusion module fuses the image features learned by the convolutional rebalancing module and ensures that the feature extraction of the imbalanced dataset is more comprehensive. Extensive experiments in the large-scale imbalanced dataset of rice pests and diseases (18,391 images), publicly available plant image datasets (Flavia, Swedish Leaf, and UCI Leaf) and pest image datasets (SMALL and IP102) verify the robustness of the proposed network, and the results demonstrate its superior performance over state-of-the-art methods, with an accuracy of 97.58% on rice pest and disease image dataset. We conclude that the proposed network can provide an important tool for the intelligent control of rice pests and diseases in the field.

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Section: Introductionmentioning

confidence: 99%

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

et al. 2021

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Whither rice health in the lowlands of Asia: Shifts in production situations, injury profiles, and yields

et al. 2021

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China were paid when no-one was ill, but payment was stopped when illness occurred (Clapier-Valladon, 1990). "Health" and "beauty" are often related: in sub-Saharan Africa, women's beauty is often linked to two characteristics: the smoothness and grain of the skin and being fat, very much in contrast with the Western standard of beauty: thinness sometimes bordering sickliness (Eco, 2004). In many parts of China, in the Philippines, and in the Ivory Coast, a healthy child is often a fat one (S. Savary, personal observation, West Africa, 1980Africa, -1990. Health is a relative notion in humans depending on cultures and past experience (of poverty, of hunger, and of disease). Health is also relative over time: one is more or less healthy. This relativeness does not exist in disease: one is ill or not.Plant health is even harder to define (Döring et al., 2012). Like small children, sick plants cannot speak; they cannot point at part of their tissues where sickness might reside. One can only declare

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The Impact of Rice Diseases in Tropical Asia

Cited by 2 publications

References 56 publications

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Convolutional Rebalancing Network for the Classification of Large Imbalanced Rice Pest and Disease Datasets in the Field

Whither rice health in the lowlands of Asia: Shifts in production situations, injury profiles, and yields

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