  India is agriculture based economy and sugarcane is one of the major crops
produced in northern India. Productivity of sugarcane decreases due to
inappropriate soil conditions and infections caused by various types of
diseases , timely and accurate disease diagnosis, plays an important role
towards optimizing crop yield. This paper presents a system model for
monitoring of sugarcane crop, the proposed model continuously monitor
parameters (temperature, humidity and moisture) responsible for healthy growth
of the crop in addition KNN clustering along with SVM classifier is utilized
for infection identification if any through images obtained at regular
intervals. The data has been transmitted wirelessly from the site to the
control unit. Model achieves an accuracy of 96% on a sample of 200 images, the
model was tested at Lolai, near Malhaur, Gomti Nagar Extension.
