Recognition of Blister Blight Disease in Tea Leaf Using Fully Convolutional Neural Networks
L.Leema priyadarsini1, D.Femi2
1L.Leema priyadarsini, Department of Computer Science and Engineering, Veltech Rangarajan Dr.Sakunthala R & D Institute of Science and Technology, Chennai,India.
2D.Femi, Department of Computer Science and Engineering, Veltech Rangarajan Dr.Sakunthala R & D Institute of Science and Technology, Chennai,India.
Manuscript received on 30June 2019 | Revised Manuscript received on 05July 2019 | Manuscript published on 30 July 2019 | PP: 182-185 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7702078919/19©BEIESP| DOI: 10.35940/ijitee.I7702.078919
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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: Tea plantation contributes significantly to the agricultural economy of India. Automatic tea leaf disease detection is beneficial when compared to manual detection which is not only a tedious grueling task but also less accurate and time consuming. This paper presents an alternative image segmentation technique that can be used for automatic detection and classification of blister blight diseased tea leaf using a fully convolutional neural network (CNN) based method to segment blisters in a tea leaf image. The suggested technique proves to be beneficial in monitoring large fields of tea crops.
Keywords: Segmentation technique, blister blight, convolutional neural network.
Scope of the Article: Sensor Networks, Actuators for Internet of Things