Fruit disease recognition using improved sum and difference histogram from images

Diseases in fruit cause devastating problem in production and availability. The classical approach of fruit disease recognition is based on the naked eye observation by experts. Detection of defects is still problematic due to the natural variability of colour in different types of fruits, high variance of defect types, and presence of stem/calyx. In this paper, a framework for the recognition of fruit diseases is proposed. The proposed approach is composed of the following three main steps; defect segmentation, feature extraction, and classification. This paper also introduces an improved sum and difference histogram (ISADH) texture feature based on the intensity values of the neighbouring pixels. The gradient filters are also used with ISADH in this paper to boost the discriminative ability. We have considered apple diseases as a test case and evaluated our program. Experimental results suggest that the proposed method can significantly support automatic recognition of fruit diseases. The classification accuracy has achieved more than 97% using ISADH texture feature. Our method is able to achieve nearly 99.9% of accuracy in conjunction with the gradient filters.

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