Abstract:
Deep convolutional neural network is a diverting area where researches and achievement are
taking excellent progress in agriculture field. The most recent enhancements in computer
vision formulated thorough deep learning have covered the method for how to identify and
analyze diseases in plants by utilizing a camera to capture images an basis for recognizing
several types of plant diseases. This research elaborates disease detection and classification
with help of deep learning convolutional neural network. Sugarcane is a vital crop in the
world. For detecting sugarcane diseases the researchers used the convolutional neural
networks (CNNs) as the basic deep learning method. This study trained and test deep learning
model consisting of 2200 sugarcane images dataset. After applying CNNs it achieves an
accuracy of 92% and also get error rate 8%. The trained model acquired it motive by
detecting and classifying sugarcane images into healthy and infected of sugarcane plants.
Therefore, this research provides a step of helping farmers with the process of deep learning
algorithm in detecting and classifying sugarcane diseases.
