Evaluation of deep learning Convolutional Neural Network for Crop Classification
Kavita Bhosle1, Vijaya Musande2 

1Kavita Bhosle, Department of Computer Science and Engineering, Maharashtra Institute of Technology, Aurangabad, MH, India.
2Dr. Vijaya Musande, Department of Computer Science and Engineering, MGM’s Jawaharlal Nehru Engineering College, Aurangabad, MH, India.

Manuscript received on 18 March 2019 | Revised Manuscript received on 24 March 2019 | Manuscript published on 30 July 2019 | PP: 3960-3963 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2872078219/19©BEIESP | DOI: 10.35940/ijrte.B2872.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In this paper, we have done exploratory experiments using deep learning convolutional neural network framework to classify crops into cotton, sugarcane and mulberry. In this contribution we have used Earth Observing-1 hyperion hyperspectral remote sensing data as the input. Structured data has been extracted from hyperspectral data using a remote sensing tool. An analytical assessment shows that convolutional neural network (CNN) gives more accuracy over classical support vector machine (SVM) and random forest methods. It has been observed that accuracy of SVM is 75 %, accuracy of random forest classification is 78 % and accuracy of CNN using Adam optimizer is 99.3 % and loss is 2.74 %. CNN using RMSProp also gives the same accuracy 99.3 % and the loss is 4.43 %. This identified crop information will be used for finding crop production and for deciding market strategies.
Index Terms: Convolutional Neural Network, Hyperspectral Remote Sensing Data, Random Forest Classifier, Support Vector Machine.

Scope of the Article: Classification