An Efficient Detection System of Plant Leaf Disease to Provide Better Remedy
Komal Kashyap1, K. Subhadra2, Peshimam Md Nadeem3, B. Dandy Sabarish4

1Komal Kashyap*, Assistant Professor (CSE), GITAM (Deemed to be University), Visakhapatnam, India.
2K. Subhadra, Assistant Professor (CSE), GITAM(Deemed to be University), Visakhapatnam, India.
3Peshimam Md Nadeem, UG Student, Department of CSE, GITAM (Deemed to be University), Visakhapatnam, India.
4B. Dandy Sabarish , UG Student, Department of CSE, GITAM (Deemed to be University), Visakhapatnam, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 28, 2020. | Manuscript published on April 10, 2020. | PP: 2022-2028 | Volume-9 Issue-6, April 2020. | Retrieval Number: E3030039520/2020©BEIESP | DOI: 10.35940/ijitee.E3030.049620
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Abstract: Machine learning is the one of the leading studies in Artificial Intelligence to extend research irresistibly or give the edification to a particular task to implement a scenario. The role of machine learning is to deduce the format of the data, make it feasible to design models that can be easily understood and apply them. This application could also be done in the field of agriculture in detecting the crop diseases. Plant diseases caused by microorganisms lead to serious reaping loss all-around. The most frequently effected diseases to plants are bacterial Canker, Blank knot, Brown Rot, Anthracnose, Apple Scarb etc. The prototype framework in this research model is for predicting and identifying the plant disease and provides remedies that can be used as protective measures against the disease. The implementation of the model described in this paper incorporates dense neural networks (DNN) Algorithm which is the sub part of Convolutional Neural Network (ConvNet/CNN). To build the model we have used TensorFlow DNN models. 
Keywords: CNN, DNN, Machine Learning, Plant Disease Detection.
Scope of the Article: Machine Learning