Deep Learning-Based Multiclass Classification of Diseases in Cucumber Fruit: Enhancing Agriculture Diagnosis

Asim  Mehmood; Rabia Tehseen; Ayesha Zaheer; Maham Mehr Awan

Authors

Asim Mehmood Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Rabia Tehseen Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Ayesha Zaheer Department of Computer Science, University of Central Punjab, Lahore, Pakistan
Maham Mehr Awan Department of Software Engineering, University of Central Punjab, Lahore, Pakistan

Keywords:

Cucumber diseases classification, Transfer learning, Deep learning, MobileNetv2, VGG16, ResNet50, Preprocessing, Clahe, Convolutional Neural Network (CNN), Machine learning.

Abstract

Agriculture plays a key role in the economies of many developing nations. cucumber is cultivated vegetable that are grown in large quantities, but the production is regularly affected by diseases, with its yield loss impacted by diseases which include Belly Rot and Pythium Fruit Rot. Early and accurate disease diagnosis is critical for minimizing economic losses and improving crop quality. Traditional method techniques are based on visual identification and time-consuming and often inaccurate, especially for the early stages of the disease. In this work, we aim to tackle these problems and present an automatic cucumber disease classification system by transfer learning. Three convolutional neural network models (pre-trained VGG16, MobileNetV2 and ResNet-50) were retrained on a set of 2400 images containing two disease classes and one normal class. The images were preprocessed with the Contrast Limited Adaptive Histogram Equalization (CLAHE) and background removal by deep learning segmentation to eliminate the background noise and focus only on the informative feature of the image. The models were trained and tested by using training, validation, and test sets with the respective accuracies of 95.28%, 98.06%, and 57.5%. MobileNetV2 showed superior performance to all other models including the highest precision, recall, and F1 score of 0.98, confirming that it was robust and appropriate for real-time disease classification. The results demonstrate that the transfer learning method is conducive to improving the issues of lack of labeled samples and variations in image acquisition and strength, thus providing a reliable model for early disease detection in cucumbers. The system we propose can support farmers and agronomists in early disease management decisions and reduce chemical usage. In the future, we will increase the data set with more disease classes, and develop a mobile APP for field level disease detection.

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