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This paper presents a framework which uses computer vision algorithms tostandardise images and analyse them for identifying crop diseasesautomatically. The tools are created to bridge the information gap betweenfarmers, advisory call centres and agricultural experts using the images ofdiseased/infected crop captured by mobile-phones. These images are generallysensitive to a number of factors including camera type and lighting. Wetherefore propose a technique for standardising the colour of plant imageswithin the context of the advisory system. Subsequently, to aid the advisoryprocess, the disease recognition process is automated using image processing inconjunction with machine learning techniques. We describe our proposed leafextraction, affected area segmentation and disease classification techniques.The proposed disease recognition system is tested using six mango diseases andthe results show over 80% accuracy. The final output of our system is a list ofpossible diseases with relevant management advice.

Towards automated mobile-phone-based plant pathology management