Crop Condition Assessment using Machine Learning
R. Poonguzhali1, A.Vijayabhanu2
1R. Poonguzhali, Assistant Professor, Department of Computer Science and Engineering, India.
2A. Vijayabhanu, Deputy Director, Department of Information System, EDPO, ISRO.
Manuscript received on 30 March 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 27 April 2019 | PP: 897-900 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F11040476S219/2019©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: Agriculture is considered to be the backbone of our country. Crops play an important role in our daily routine providing us with nourishments. Due to environmental conditions, crops are getting affected with many diseases. Farmers are not able to detect these diseases at an early stage. Thus, assessment of crop condition is vital. The growing technology plays a major role and techniques like Machine Learning, Deep Learning are used. This paper focuses on the assessment of the crop condition with the help of their leaves. Healthy as well as diseased leaves are captured using cameras from real-time environments. The captured images undergo processes like preprocessing and segmentation. K-means clustering is used for segmentation. After segmentation, they undergo classification using Machine learning algorithms in which healthy and diseased leaves are detected. Thus this system helps to reduce the difficulties faced by the farmers during crop cultivation which helps in increasing the crop yield.
Keywords: Crop Cultivation, Machine Learning, Deep Learning, K-means Clustering.
Scope of the Article: Machine Learning