Article: A hybrid framework for detection of diseases in apple and tomato crops with deep feed forward neural network Journal: International Journal of Sustainable Agricultural Management and Informatics (IJSAMI) 2018 Vol.4 No.3/4 pp.361 - 377 Abstract: The traditional farming methods with low productivity and crop damage due to diseases have resulted in low economic growth of the farmer. To overcome this problem, an expert system capable of monitoring the crop growth and early detection of the diseases is highly essential for the farmer to take preventive steps. In this work, firstly, the quality of the image is improved using both threshold-based and principle component analysis techniques. Then the enhanced images were divided into three individual colour channels (red, green, and blue) and then 27 statistical features comprising of texture, colour and energy are computed. The same statistical features of the first-level wavelet decomposition are appended to those 81 features. Finally, the features were classified by the deep feed forward neural network to identify type of the disease. The proposed method outperformed the existing methods by yielding 94.06% and 96.78% of accuracy on tomato and apple datasets respectively. Inderscience Publishers - linking academia, business and industry through research

Title: A hybrid framework for detection of diseases in apple and tomato crops with deep feed forward neural network

Authors: R. Praneetha; S. Venkatramaphanikumar; K.V. Krishna Kishore

Addresses: Department of Computer Science and Engineering, Vignans Nirula Institute of Technology and Science for Women, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India

Abstract: The traditional farming methods with low productivity and crop damage due to diseases have resulted in low economic growth of the farmer. To overcome this problem, an expert system capable of monitoring the crop growth and early detection of the diseases is highly essential for the farmer to take preventive steps. In this work, firstly, the quality of the image is improved using both threshold-based and principle component analysis techniques. Then the enhanced images were divided into three individual colour channels (red, green, and blue) and then 27 statistical features comprising of texture, colour and energy are computed. The same statistical features of the first-level wavelet decomposition are appended to those 81 features. Finally, the features were classified by the deep feed forward neural network to identify type of the disease. The proposed method outperformed the existing methods by yielding 94.06% and 96.78% of accuracy on tomato and apple datasets respectively.

Keywords: principle component analysis; PCA; statistical features; DFFNN; wavelets; precision agriculture.

DOI: 10.1504/IJSAMI.2018.099222

International Journal of Sustainable Agricultural Management and Informatics, 2018 Vol.4 No.3/4, pp.361 - 377

Received: 19 Sep 2018
Accepted: 06 Nov 2018
Published online: 23 Apr 2019 *

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Title: A hybrid framework for detection of diseases in apple and tomato crops with deep feed forward neural network

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