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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Cucumis callosus (Rottl.) Cogn. leaf classification and identification using deep learning: A novel agricultural dataset

DOI
https://doi.org/10.14719/pst.11638
Submitted
5 September 2025
Published
28-11-2025

Abstract

Agriculture is fundamental to human civilization and every plant has a unique life cycle influenced by environmental conditions, species and growth stage. Early identification of leaf stages and health status can prevent plant loss. Still, manual classification is time-consuming and may not accurately detect diseases or determine the correct life cycle stage. This study uses transfer learning to detect Cucumis callosus (Rottl.) Cogn. (herbaceous plant) leaves using base models, including Densely Connected Convolutional Networks (DenseNet121), InceptionV3, MobileNet and
Extreme Inception Network (XceptionNet). Transfer learning plays an important role in ensuring high accuracy in results. Data augmentation techniques were employed to balance the images into categories. This study proposes a lightweight leaf-recognizing convolutional neural network (CNN) model. In comparison, 4 pre-trained models, DenseNet121, InceptionV3, MobileNet and XceptionNet, are used to validate the proposed approach results. The training and validation accuracies are nearly the same for all models. It ranges from 95 to 99 %. The proposed
CNN achieved 100 % testing and validation accuracy with 285027 trainable parameters, demonstrating its suitability for future applications. The proposed CNN offers a reliable, practical and portable method for identifying C. callosus leaves.

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