Image-Based Plant Disease Detection with Deep Learning
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : Ashwin Dhakal, Prof. Dr. Subarna Shakya|
|DOI : 10.14445/22312803/IJCTT-V61P105|
MLA Style: Ashwin Dhakal, Prof. Dr. Subarna Shakya "Image-Based Plant Disease Detection with Deep Learning" International Journal of Computer Trends and Technology 61.1 (2018): 26-29.
APA Style:Ashwin Dhakal, Prof. Dr. Subarna Shakya, (2018). Image-Based Plant Disease Detection with Deep Learning. International Journal of Computer Trends and Technology, 61(1), 26-29.
Deep Learning becomes the most accurate and precise paradigms for the detection of plant disease. Leaves of Infected crops are collected and labelled according to the disease. Processing of image is performed along with pixel-wise operations to enhance the image information. It is followed with feature extraction, segmentation and the classification of patterns of captured leaves in order to identify plant leaf diseases. Four classifier labels are used as Bacterial Spot, Yellow Leaf Curl Virus, Late Blight and Healthy Leaf. The features extracted are fit into the neural network with 20 epochs. Several artificial neural network architectures are implemented with the best performance of 98.59% accuracy in determining the plant disease. This was a great success, demonstrating the feasibility of this approach in the field of Plant Disease Diagnosis and high crop yielding.
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Convolutional neural network, Deep learning,Plant disease detection, Image processing, Machine learning