Precision Sugarcane Monitoring Using SVM Classifier

India is an 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 toward...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.122, p.881-887
Main Authors: Kumar, Sachin, Mishra, Sumita, Khanna, Pooja, Pragya
Format: Article
Language:English
Subjects:
Humidity Sensor
Image Processing
KNN
Moisture Sensor
Precision agriculture
Support vector Machine
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Summary:India is an 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 monitors 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 through a wireless network 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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.11.450