Identification of Sugarcane Foliar Diseases: Methods and Datasets
Swapnil Dadabhau Daphal1, S. M. Koli2

1Swapnil Dadabhau Daphal*, Research Scholar, Assistant Professor, G. H . Raisoni College of Engineering and Management, Pune India.
2Dr. S. M. Koli, Research Supervisor, Professor, Dept. of E&TC G. H . Raisoni College of Engineering and Management, Pune India.
Manuscript received on January 22, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 4305-4311 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C6454029320/2020©BEIESP | DOI: 10.35940/ijeat.C6454.029320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Agriculture is the major part of the Indian economy as it provides key support to social and economic development of the country. Sugarcane is the leading cash crop in various states of India which has larger share in the net agriculture produce. Recently, researches have highlighted the impact of different disease on various plants. Estimated loss is much severe for the sugarcane crop via foliar diseases. Foliar diseases like rust, eye spot, mosaic and banded chlorosis may hamper the overall productivity of sugarcane and sugar recovery rate (RR). Early prediction of these diseases may limit the losses in terms of produce net benefits. This paper addresses the concerns, types of the foliar disease and researches undertaken to overcome the problems related to the diseases. Morphological characters of these diseases may help in identifying the representative features and to use them for the optimum classification. Currently the use of deep neural networks (DNN) is encouraged for the classification. DNN demands the huge and accurate databases. Intuitively the use and important methods used in database creation for disease diagnostic system (DDS) has been highlighted in the paper. Modifications made to the Convolutional Neural Network architecture have suggested the improved performance in terms of recognition accuracy (RA) and lesser recognition time.
Keywords: Sugarcane, Deep Neural Network (DNN), Recovery rate (RR), recognition accuracy (RA), Video Signal Processing, Disease Diagnostic System (DDS).