Plant Disease Detection using Deep Learning
Murk Chohan1, Adil Khan2, Rozina Chohan3, Saif Hassan Katpar4, Muhammad Saleem Mahar5
1Murk Chohan*, Department of Computer Science, Sukkur IBA University, Pakistan.
2Adil Khan, Department of Computer Science, Sukkur IBA University, Pakistan.
3Rozina Chohan, Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan.
4Saif Hassan Katpar, Department of Computer Science, Sukkur IBA University, Pakistan.
5Muhammad Saleem Mahar, Department of Computer Science, Sukkur IBA University, Pakistan.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 909-914 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2139059120/2020©BEIESP | DOI: 10.35940/ijrte.A2139.059120
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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: [Context] Plants play an essential role in climate change, agriculture industry and a country’s economy. Thereby taking care of plants is very crucial. Just like humans, plants are effected by several disease caused by bacteria, fungi and virus. Identification of these disease timely and curing them is essential to prevent whole plant from destruction. [Objective] This paper proposes a deep learning based model named plant disease detector. The model is able to detect several diseases from plants using pictures of their leaves. [Methodology] Plant disease detection model is developed using neural network. First of all augmentation is applied on dataset to increase the sample size. Later Convolution Neural Network (CNN) is used with multiple convolution and pooling layers. Plant Village dataset is used to train the model. After training the model, it is tested properly to validate the results. [Results] We have performed different experiments using this model. 15% of data from Plant Village data is used for testing purpose that contains images of healthy as well as diseased plants. Proposed model has achieved 98.3% testing accuracy. [Conclusion] This study is focused on deep learning model to detect disease in plant leave. But, in future model can be integrated with drone or any other system to live detect diseases from plants and report the diseased plants location to people so that they can be cured accordingly.
Keywords: Plant Disease, Convolution Neural Network (CNN), Deep Learning, Agriculture, and Plant Village.
Scope of the Article: Deep Learning