Graph cuts segmentation enhances machine-assisted plant disease diagnosis under tight data constraints

Takang Achuo Albert Enow, Hermine Bille Ngalle and Mangaptche Eddy Leonard Ngonkeu

International Journal of Complexity in Applied Science and Technology, 2026, vol. 2, issue 1, 39-62

Abstract: Effective plant disease diagnosis is key to sustainable farming, but data scarcity remains a significant hurdle. This research examined machine learning models trained on very small datasets enhanced by varied image processing techniques to identify the most reliable approach. The Random Walk Segmented dataset initially seemed more promising, excelling in both precision and accuracy. However, its performance faltered with a random-like ROC-AUC score, suggesting unreliability. In contrast, the Graph Cuts Segmented dataset, despite trailing in precision and accuracy, demonstrated greater consistency with a higher ROC-AUC score. These results highlight the critical need to use diverse metrics for evaluating machine learning models, emphasising that reliability cannot rely solely on accuracy. The study sheds light on advancing plant disease diagnosis in environments constrained by limited data, paving the way for more robust solutions tailored to resource-scarce contexts.

Keywords: data scarcity; plant disease diagnosis; machine learning; graph cuts segmentation; random walk. (search for similar items in EconPapers)
Date: 2026
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