Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks

Abstract

Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management.

Plant phenotyping relevance

油ヤシ幹の病徴を画像から色・テクスチャ特徴として抽出し、分類器で病害状態を推定する方法が研究の中心であり、交差検証による性能評価も行っているため、植物表現型計測手法として含める。

Code and data availability

The supplied blocks describe a 525-image oil palm stem dataset collected by the authors and an ANN classification pipeline, but contain no availability statement, deposit, or public URL for the dataset, images, code, or trained model. No paper-specific public asset is actionable.

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