Authors:
Prakruti Bhatt
;
Sanat Sarangi
;
Anshul Shivhare
;
Dineshkumar Singh
and
Srinivasu Pappula
Affiliation:
TCS Research and Innovation, Mumbai and India
Keyword(s):
Disease Classification, Adaptive Boosting, Ensemble Classifier, CNN Features.
Abstract:
Precision farming technologies are essential for a steady supply of healthy food for the increasing population around the globe. Pests and diseases remain a major threat and a large fraction of crops are lost each year due to them. Automated detection of crop health from images helps in taking timely actions to increase yield while helping reduce input cost. With an aim to detect crop diseases and pests with high confidence, we use convolutional neural networks (CNN) and boosting techniques on Corn leaf images in different health states. The queen of cereals, Corn, is a versatile crop that has adapted to various climatic conditions. It is one of the major food crops in India along with wheat and rice. Considering that different diseases might have different treatments, incorrect detection can lead to incorrect remedial measures. Although CNN based models have been used for classification tasks, we aim to classify similar looking disease manifestations with a higher accuracy compared
to the one obtained by existing deep learning methods. We have evaluated ensembles of CNN based image features, with a classifier and boosting in order to achieve plant disease classification. Using an ensemble of Adaptive Boosting cascaded with a decision tree based classifier trained on features from CNN, we have achieved an accuracy of 98% in classifying the Corn leaf images into four different categories viz. Healthy, Common Rust, Late Blight and Leaf Spot. This is about 8% improvement in classification performance when compared to CNN only.
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