Volume V | Issue 3 | 2026 | Paper ID: IJATEM-V05I03P3

Automated Olive Leaf Disease Identification using K-Means segmentation and VGG-19 –based Classification

Author(s)

, J N Rajesh Kumar

Cite This

"Automated Olive Leaf Disease Identification using K-Means segmentation and VGG-19 –based Classification", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 03, pp. 20-26, 2026.

Abstract

Early and accurate detection of Olive Leaf Diseases (OLD) is essential for effective agricultural management and significantly affect crop productivity and quality. This paper proposed a VGG-19-based classification a Deep Learning (DL) model, for automated OLD identification. Preprocessing based on image resizing and Gaussian Filtering (GF) to enhance image quality and reduce noise. Segmentation based on K-Means segmentation is used to accurately isolate the diseased regions from the leaf background. Feature extraction is then performed by the Histogram of Oriented Gradients (HOG) technique to capture discriminative texture and shape characteristics associated with different disease patterns. VGG-19 is proposed which effectively classifies the complex visual representations for disease recognition. Experimental results demonstrate that the proposed framework attains high classification accuracy of 94.8% and precision, recall and F1-score of 95% and robust performance in identifying various OLD. The novelty of the paper enhances disease recognition accuracy while reducing the influence of background noise.

Keywords

Olive Leaf Diseases VGG-19, Gaussian Filtering, K-Means, Histogram of Oriented Gradients

References

[1] Konstantinos Prousalidis; Stavroula Bourou; Terpsichori-Helen Velivassaki; Artemis Voulkidis; Aikaterini Zachariadi; Vassilios Zachariadis, Year: 2024, “Olive tree segmentation from UAV imagery”, Drones, Vol: 8, No: 8, pp. 408.

CrossRef Google Scholar Publisher Link
[2] Antonio Fazari; Oscar J. Pellicer-Valero; Juan Gómez-Sanchıs; Bruno Bernardi; Sergio Cubero; Souraya Benalia, Giuseppe Zimbalatti, and Jose Blasco, Year: 2021, “Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images”, Computers and Electronics in Agriculture, Vol: 187, pp. 106252.

CrossRef Google Scholar Publisher Link
[3] Konstantinos N. Blazakis; Danil Stupichev; Maria Kosma; Mohamad Ali Hassan El Chami; Anastasia Apodiakou; George Kostelenos; Panagiotis Kalaitzis, Year: 2024, “Discrimination of 14 olive cultivars using morphological analysis and machine learning algorithms”, Frontiers in Plant Science, Vol: 15, pp. 1441737.

CrossRef Google Scholar Publisher Link
[4] Mohamed Lachgar; Hamid Hrimech; Ali Kartit, Year: 2022, “Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification”, Artificial Intelligence in Agriculture, Vol: 6, pp. 77-89.

CrossRef Google Scholar Publisher Link
[5] Hristofor Miho; Giulio Pagnotta; Dorjan Hitaj; Fabio De Gaspari; Luigi Vincenzo Mancini; Georgios Koubouris; Gianluca Godino; Mehmet Hakan; Concepción Muñoz Diez, Year: 2024, “OliVaR: Improving olive variety recognition using deep neural networks”, Computers and Electronics in Agriculture, Vol: 216, pp. 108530.

Google Scholar Publisher Link
[6] Ali Hakem Alsaeedi; Ali Mohsin Al-Juboori; Haider Hameed R; Al-Mahmood; Suha Mohammed Hadi; Husam Jasim Mohammed; Mohammad R. Aziz; Mayas Aljibawi; Riyadh Rahef Nuiaa, Year: 2023, “Dynamic clustering strategies boosting deep learning in olive leaf disease diagnosis”, Sustainability, Vol: 15, No: 18, pp. 13723.

CrossRef Google Scholar Publisher Link
[7] Sivakumar Rajendran, Year: 2025, “Enhancing Cotton Leaf Disease Detection using Deep Learning Approach: A Study and Comparison”, In 2025 International Conference on Electronics and Renewable Systems (ICEARS), pp. 1450-1454. IEEE.

CrossRef Google Scholar Publisher Link
[8] Amel Ksibi; Manel Ayadi; Ben Othman Soufiene; Mona M. Jamjoom; Zahid Ullah, Year: 2022, “MobiRes-net: a hybrid deep learning model for detecting and classifying olive leaf diseases”, Applied Sciences, Vol. 12, No. 20, pp. 10278.

CrossRef Google Scholar Publisher Link
[9] Hamoud H.Alshammari; Ahmed I. Taloba; Osama R. Shahin, Year: 2023, “Identification of olive leaf disease through optimized deep learning approach”, Alexandria Engineering Journal, Vol. 72, pp. 213-224.

CrossRef Google Scholar Publisher Link
[10] Saul Huaquipaco; Oscar Vera; Victor Yana-Mamani; Wilson Mamani; Helarf Calsina; Flavio Puma; Eli Morales-Rojas; Norman Beltran; Jose Cruz, Year: 2024, “Peacock spot detection in Olive leaves using self supervised learning in an assembly Meta-Architecture”, IEEE Access, Vol: 12, pp. 192828-192839.

CrossRef Google Scholar Publisher Link
[11] Ishak Pacal; Serhat Kilicarslan; Burhanettin Ozdemir; Muhammet Deveci; Seifedine Kadry, Year: 2025, “Efficient and autonomous detection of olive leaf diseases using AI-enhanced MetaFormer”, Artificial Intelligence Review, Vol: 58, No: 10, pp. 303.

CrossRef Google Scholar Publisher Link
[12] Hakan Gunduz, Year: 2026, “Efficient Olive Leaf Disease Detection Using Composite Feature Selection and Ensemble Learning”, Agronomy, Vol: 16, No: 11, pp. 1057.

CrossRef Google Scholar Publisher Link

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