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Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision agriculture, environmental monitoring using bio-indicators and quality assessment of leafy vegetables. Leaf injury is usually assessed by visual inspection, which is labour-intensive and to a considerable extent subjective. In this study, methods for classifying individual pixels as healthy or injured from images of clover leaves exposed to the air pollutant ozone were tested and compared. RGB images of the leaves were acquired under controlled conditions in a laboratory using a standard digital SLR camera. Different feature vectors were extracted from the images by including different colour and texture (spatial) information. Four approaches to classification were evaluated: (1) Fit to a Pattern …