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Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning

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Abstract

Among the fruit harvest, banana is one of the most significant crops in the international business for yielding the foreign exchange in many African countries. Nowadays, banana leaf diseases are the most significant factors regarding agricultural goods. These diseases result in a severe decline in the quantity and quality of agricultural food. Moreover, early recognition and classification of these banana leaf diseases are much needed. In this work, a novel deep learning technique called Heap Auto Encoders (HAEs) has been proposed. This proposed method can straightly extract important features and reduce the exhausted use of handcrafted features. Besides, over fitting difficulty in the training method is reduced, and the performance for a small training set is improved. The dropout method and the Rectified Linear Units (ReLU) activation function are also applied in HAE. The results of the proposed technique indicate that the proposed method is superior to traditional techniques. This framework provides the highest classification accuracy of 99.35% for real data sets.

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Authors and Affiliations

  1. Computer Science, Sarah Tucker College, Plot 6,7 K. L. N Colony, Tirunelveli, Tamil Nadu, 627007, India

    Ani Brown Mary N

  2. School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar, Krishnankoil, Tamil Nadu, India

    Robert Singh A.

  3. School of Computing, Sastra Deemed to be University, Plot 6,7 K. L. N Colony, Thanjavur, Tamil Nadu, India

    Suganya Athisayamani

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  1. Ani Brown Mary N
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  2. Robert Singh A.
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  3. Suganya Athisayamani
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Correspondence to Ani Brown Mary N .

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Ani Brown Mary N, Robert Singh A. & Athisayamani, S. Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning. Multimed Tools Appl 79, 30601–30613 (2020). https://doi.org/10.1007/s11042-020-09521-1

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  • DOI: https://doi.org/10.1007/s11042-020-09521-1

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