Detection and Classification of Plant Disease using Deep Learning
E.Jane Marie Harshitha, M.Nandini Devi, A.Prathema Rachil, P.Ramya, SUMATHI
Plant disease prediction, Features extraction, Classification, Fertilizer Recommendation, Neural network approach
The purpose of Agriculture is not only to feed ever growing population but it’s an important source of energy and a solution to solve the problem of global warming. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Generally the naked eye method is used to identify the diseases. In this method experts are involved who have the ability to detect the changes in leaf color. This method involves lots of efforts, takes long time and also not practical for the large fields. Many times different experts identify the same disease as the different disease. This method is expensive as it requires continuous monitoring of experts. Plant diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. The producers need to monitor their crops and detect the first symptoms in order to prevent the spread of a plant disease, with low cost and save the major part of the production. Hiring professional agriculturists may not be affordable especially in remote isolated geographic regions. Machine learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes features extraction and classification which includes local binary method and discrete wavelet transform algorithm and also image classification approach which includes neural network algorithm to predict various types of diseases. And also extend the approach to recommend the fertilizers based on severity analysis with measurements.
Article Details
Unique Paper ID: 149693

Publication Volume & Issue: Volume 7, Issue 1

Page(s): 332 - 339
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