A comprehensive lemongrass (Cymbopogon citratus) leaf dataset for agricultural research and disease prevention

Patil, Kailas; Suryawanshi, Yogesh; Patrawala, Alimurtuza; Chumchu, Prawit

doi:10.1016/j.dib.2024.110104

Data in Brief

2024

DOI: 10.1016/j.dib.2024.110104

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A comprehensive lemongrass (Cymbopogon citratus) leaf dataset for agricultural research and disease prevention

Kailas Patil,

Yogesh Suryawanshi,

Alimurtuza Patrawala

et al.

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Cited by 3 publications

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A comprehensive dataset of eight Thai cannabis classes for botanical exploration

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Chumchu

2024

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A comprehensive dataset of eight Thai cannabis classes for botanical exploration

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2024

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Detecting dried Oudemansiella raphanipies using RGB imaging: A case study

Yin,

Zhao,

Lei

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Recognition Method of Crop Disease Based on Image Fusion and Deep Learning Model

Ma,

Zhang,

Guan

et al. 2024

Agronomy

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Accurate detection of early diseased plants is of great significance for high quality and high yield of crops, as well as cultivation management. Aiming at the low accuracy of the traditional deep learning model for disease diagnosis, a crop disease recognition method was proposed based on multi-source image fusion. In this study, the adzuki bean rust disease was taken as an example. First, color and thermal infrared images of healthy and diseased plants were collected, and the dynamic thresholding excess green index algorithm was applied to extract the color image of the canopy as the reference image, and the affine transformation was used to extract the thermal infrared image of the canopy. Then, the color image was fused with the thermal infrared image by using a linear weighting algorithm to constitute a multi-source fusion image. In addition, the sample was randomly divided into a training set, validation set, and test set according to the ratio of 7:2:1. Finally, the recognition model of adzuki bean rust disease was established based on a novel deep learning model (ResNet-ViT, RMT) combined with the improved attention mechanism and the Squeeze-Excitation channel attention mechanism. The results showed that the average recognition rate was 99.63%, the Macro-F1 was 99.67%, and the recognition time was 0.072 s. The research results realized the efficient and rapid recognition of adzuki bean rust and provided the theoretical basis and technical support for the disease diagnosis of crops and the effective field management.

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A comprehensive lemongrass (Cymbopogon citratus) leaf dataset for agricultural research and disease prevention

Cited by 3 publications

References 7 publications

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

A comprehensive dataset of eight Thai cannabis classes for botanical exploration

Detecting dried Oudemansiella raphanipies using RGB imaging: A case study

Recognition Method of Crop Disease Based on Image Fusion and Deep Learning Model

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