Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

Alotaibi, Saud S.

doi:10.32604/iasc.2023.029446

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The Explainer object is created using the model, data, and target in the seventh line. The eighth line employs the Shap.plots.bar () function to plot the SHAP values [38].…”

Section: Shap Analysismentioning

confidence: 99%

Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Selvanarayanan,

Rajendran,

Alotaibi

2024

CMES

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Colletotrichum kahawae (Coffee Berry Disease) spreads through spores that can be carried by wind, rain, and insects affecting coffee plantations, and causes 80% yield losses and poor-quality coffee beans. The deadly disease is hard to control because wind, rain, and insects carry spores. Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93% accuracy using a random forest method. If the dataset is too small and noisy, the algorithm may not learn data patterns and generate accurate predictions. To overcome the existing challenge, early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes, prompt recognition, and accurate classifications. The proposed methodology selects CBD image datasets through four different stages for training and testing. XGBoost to train a model on datasets of coffee berries, with each image labeled as healthy or diseased. Once the model is trained, SHAP algorithm to figure out which features were essential for making predictions with the proposed model. Some of these characteristics were the cherry's colour, whether it had spots or other damage, and how big the Lesions were. Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease. To evaluate the model's performance and mitigate excess fitting, a 10-fold cross-validation approach is employed. This involves partitioning the dataset into ten subsets, training the model on each subset, and evaluating its performance. In comparison to other contemporary methodologies, the model put forth achieved an accuracy of 98.56%.

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“…The Explainer object is created using the model, data, and target in the seventh line. The eighth line employs the Shap.plots.bar () function to plot the SHAP values [38].…”

Section: Shap Analysismentioning

confidence: 99%