A dataset for successful recognition of cucumber diseases

Sultana, Nusrat; Shorif, Sumaita Binte; Akter, Morium; Uddin, Mohammad Shorif

doi:10.1016/j.dib.2023.109320

Cited by 14 publications

(2 citation statements)

References 3 publications

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“…The data utilized in this investigation are accessible to the public [4]. There exist eight distinct categories of cucumber plant ailments, which include Anthracnose, Bacterial Wilt, Belly Rot, Downy Mildew, Pythium Fruit Rot, Gummy Stem Blight, Fresh leaves, and Fresh cucumber.…”

Section: Methodsmentioning

confidence: 99%

“…Typically, the identification of diseases in cucumbers relies on visual observation by individuals. However, many diseases exhibit inconspicuous symptoms, rendering their detection challenging in the absence of specialized expertise [4]. Cucumber infections exhibit discernible physical attributes, rendering them detectable through the utilization of a camera equipped with an algorithm designed to classify the specific type of disease affecting cucumbers.…”

Section: Introductionmentioning

confidence: 99%

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Cucumber Disease Classification with Ensemble Learning Method for Complex Datasets

Junior,

Majiid

2023

j. Teknik inform.

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Many researchers are taking into account the algorithm's ability to detect diseases in plants since it can save expenses and deliver more accurate results. However, there are various obstacles in detecting diseases, particularly in cucumber plants, such as disease similarities and the ability of models to adapt to the information they have. To address this issue, we propose an ensemble learning strategy based on the averaging method to improve the model's ability to generalize to different cucumber plant environments. According to the results, the ensemble learning approach outperforms the feature fusion method with a test accuracy of 94.20% and a loss of 0.01105. Feature fusion and ensemble learning techniques, in general, have the potential to increase the model's capacity to classify difficult data.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%