TITLE:
Contribution to the Automatic Detection of Coffee Plant Diseases through Image Processing and Machine Learning: A Study of Infected Leaves in Western Ivory Coast
AUTHORS:
Toa Bi Guié, Kouakou Anicet, Pavel Yale, Kouassi Yao, Kadja Anicet, Sié Ouattara, Alain Clement
KEYWORDS:
Coffee Leaf Diseases Image Processing, Machine Learning, Random Forest, SqueezeNet
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.17 No.10,
October
26,
2026
ABSTRACT: Coffee leaf diseases can cause substantial production losses in coffee-growing areas of western Côte d’Ivoire, while accessible image-based diagnostic tools remain limited in the Tonkpi region. This study investigates an image-processing and machine-learning pipeline for the detection and symptom-based classification of coffee leaf disorders under field conditions. A dataset of 1,461 coffee leaf images was collected between January 9 and 28, 2026, at four locations in the Tonkpi region using a Tecno Spark 20C smartphone and a standardized image-acquisition protocol. Two segmentation approaches, Otsu thresholding and K-means clustering (K = 3), were compared according to processing time and the downstream discriminative power of extracted descriptors. A set of 53 numerical descriptors was extracted from the segmented regions and evaluated using ANOVA. Random Forest and k-NN were used for binary healthy/diseased classification, while SqueezeNet with transfer learning was used for six-class image-based classification. Otsu required approximately 0.02 s per image, compared with approximately 0.11 - 0.12 s for K-means. The highest F-ANOVA value obtained from the Otsu-derived descriptors was 112.83 for pct_necrose_R, whereas the highest value reported for the K-means-derived descriptors was 27.00 for sigma_infecte. These F-ANOVA results are interpreted as a proxy for the usefulness of the resulting feature representations for subsequent classification rather than as direct evidence of segmentation accuracy, because manually annotated lesion masks were not available. Random Forest achieved 100% accuracy on the reported binary test set, while k-NN achieved 90.75% under the selected configuration. SqueezeNet achieved 94.54% accuracy on the six-class coffee test set. The results demonstrate the feasibility of combining classical image descriptors and lightweight transfer learning for image-based coffee leaf symptom classification in the Tonkpi dataset. Further validation with expert-verified labels, group-level data splitting, manually annotated lesion masks, and laboratory confirmation of causal pathogens is required before operational deployment.