This paper presents a framework which uses computer vision algorithms to
standardise images and analyse them for identifying crop diseases
automatically. The tools are created to bridge the information gap between
farmers, advisory call centres and agricultural experts using the images of
diseased/infected crop captured by mobile-phones. These images are generally
sensitive to a number of factors including camera type and lighting. We
therefore propose a technique for standardising the colour of plant images
within the context of the advisory system. Subsequently, to aid the advisory
process, the disease recognition process is automated using image processing in
conjunction with machine learning techniques. We describe our proposed leaf
extraction, affected area segmentation and disease classification techniques.
The proposed disease recognition system is tested using six mango diseases and
the results show over 80% accuracy. The final output of our system is a list of
possible diseases with relevant management advice.
Nantheera Anantrasirichai, Sion Hannuna, Nishan Canagarajah