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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Hyperparameter tuning of CNN based potato plant disease detection model integrated with GridSearchCV

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pp. 1–14Vol. 46Issue 1January 2025DOI: 10.47974/JIOS-1846XML
Received:
07 Aug 2024
Published Online:
19 Feb 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1846
Pages:
1–14

Abstract

There is an increasing need for enhancing plant health in the potato crop since Potato is the staple diet for majority of population living in Asia. Potato crop yield is of prime importance for farmers and agriculturalist. The Deep Convolution Neural Network model has been optimized on the PV dataset for classification, detection and prediction of various Potato Plant Diseases like PEB, PLB and distinguishing it from healthy potato leaves which served as the class labels. Images were then preprocessed using various Data augmentation techniques for enhancing the computational efficacy of the models. This paper aims at comparing various hyperparameter tuned cases for hyperparameters namely optimizer, learning rate, epochs, keeping batch_size, filter_size, activation function and the number of channels fixed for CNN as the base model integrated with GridSearchCV technique. Amongst all the hyperparameters, it was found that optimizer Adam along with 0.0001 learning rate, 3*3 filter size, 32 batch size, 100 epochs and relu activation function at dense layer and softmax function at fully connected layer performed the most optimal. The CNN base model was trained and tested to predict the output class. The efficiency of the model was measured by accuracies achieved for the model including train_accuracy, validation accuracy and test accuracy as 0.959, 0.960 and 0.988 respectively. The losses for the model such as train_loss, validation loss and test loss were 0.102, 0.100 and 0.039 respectively. After comparing all the model cases, Precision was found out to be 0.988, F1 score was found to be 0.988, Recall was 0.988, Roc-auc value was 0.563. To further comprehend the performance and select the most optimal model GridSearchCV technique was then applied for measuring performance with regards to achieving Generalization, Regularization and Optimization in the model. With a high cross-validation score of 0.78 indicates that the model can function well on unknown data, regularization as indicated by high value of C i.e. 31.622, is more tolerant of misclassifications, potentially leading to higher accuracy but potentially also lower sensitivity to detecting positive cases and optimization as the model’s parameters were successfully optimized by the ‘newton-cg’ solver, resulting in strong performance.

Keywords

Deep convolution neural networksGridSearchCVPlant disease detectionPotato early blightPotato late blightPotato healthy leaves

Subject Classifications

68T0568T07

References

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