Image based Plant leaf disease detection using Deep learning

  • Poornam S Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India.
  • Francis Saviour Devaraj A Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India.
Keywords: Plant disease, Deep learning, CNN, Classification, Artificial Intelligence


Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops.  There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs.


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How to Cite
S, P., & A, F. S. D. (2021). Image based Plant leaf disease detection using Deep learning. International Journal of Computer Communication and Informatics, 3(1), 53-65.

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