Plant Variety and Weed Growth Identification: Trending Towards Machine Learning
Andrews Samraj1, Ramesh Kumarasamy2, Nandhakumar. T3, Kamalraj. T4, Ragupathi.P5
1Prof. Dr. S. Andrews, is the Professor of Computer Science and Engineering at Mahendra Engineering College, Namakkal, India
2Mr. K. Ramesh, He is Currently Working as Assistant Professor in Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, (Tamil Nadu), India.
3T. Nandhakumar is Currently Pursuing B.E Degree in Electronics and Communivcation Engineering in Mahendra Engineering College, Namakkal, (Tamil Nadu), India.
4T. Kamalraj is Currently Pursuing B.E Degree in Electronics and Communivcation Engineering in Mahendra Engineering College, Namakkal, (Tamil Nadu), India.
5P. Ragupathi is Currently Pursuing B.E Degree in Electronics and Communivcation Engineering in Mahendra Engineering College, Namakkal, (Tamil Nadu), India.
Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 6232-6237 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3293078219/2019©BEIESP | DOI: 10.35940/ijrte.B3293.078219
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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: An important module in the agriculture 4.0 based plant monitoring is the weed growth control. In order to achieve the optimum profit on vegetable plantations the control of weeds plays an important role to ensure the precision of yield. Previous studies uses, ariel or portrait images in groups to identify the plants, weed infestation as well as intrusion detection. The motivation in this work of automation is to make the process as an autonomous system to upgrade it to agriculture 4.0 standards, by introducing Artificial Intelligence components in plant monitoring process to help the farmers with the trending technologies. This proposed research approach improves the accuracy of finding plant features from the images captured on vantage angles of the plant. We tried to classify the plants as well as the weeds through inclusion of portrait and ariel images for better classification and to aid automation that uses machine learning in plant and weed identification. Results obtained from the proposed AI system found to be appropriate and accurate in every classes of comparison.
Index Terms: Agriculture 4.0, Convolutional Neural Network, Image Processing, Machine Learning.
Scope of the Article: Machine Learning.