Optimizing Random Forest to Detect Disease in Apple Leaf
Kamalalochana S1, Nirmala Guptha2
1Kamalalochana S, Department of C&IT, REVA University, India.
2Dr. Nirmala Guptha, Department of C&IT, REVA University, India.
Manuscript received on 05 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 29 June 2019 | PP: 244-249 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10490585S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Green Revolution was introduced in agriculture to meet the food scarcity. Despite the increase of agricultural production, farmers are challenged by infestations. Infestation reduced the crop yield. Traditional method involved manual inspection of plants to identify diseases. With advancement in technology, the infested plant leaves can be captured into images and subjected to processing by computing element. The computing system are being trained to process the image using Machine Learning algorithms to classify the images. Processing the image and detecting with improved accuracy is essential. Random Forest classifier is used to detect the disease in Apple Leaf. The accuracy of prediction by Random Forest can be influenced by configuring its parameters. This Paper talks about the various options that can be applied to optimize Random Forest classifier for improving the accuracy of detecting Apple Leaf disease.
Keywords: Machine Learning Algorithm, Random Forest, Apple Leaf Disease Detection.
Scope of the Article: Machine Learning