Deep Learning for Image Based Mango Leaf Disease Detection
Sampada Gulavnai1, Rajashri Patil2

1Dr. Mrs. Sampada Gulavnai, Associate Professor, Bharati Vidyapeeth Deemed to be University, Pune, Institude of Management, Kolhapur (Maharashtra), India.
2Ms. Rajashri Patil, Associate Professor, Vivekanand College, Kolhapur (Maharashtra), India.
Manuscript received on 24 November 2019 | Revised Manuscript received on 05 December 2019 | Manuscript Published on 16 December 2019 | PP: 54-56 | Volume-8 Issue-3S3 November 2019 | Retrieval Number: C10301183S319/2019©BEIESP | DOI: 10.35940/ijrte.C1030.1183S319
Open Access | Editorial and Publishing 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 (

Abstract: Among world’s mango producing countries, India ranks first and account 50% of the world’s mango production. The mango fruit is popular because of its wide range of adaptability, high nutritional value, different variety, delicious taste and excellent flavor. The fruit contains vitamin A and vitamin C in a rich extent. The crop is prone to diseases like powdery mildew, anthracnose, die back, blight, red rust, sooty mould, etc. Disorders may also impact the plant in the absence of effective case and control measures. These include change of form, biennial bearing, fall of fruit, black top, clustering, etc. The farmer must consult and take professional support for the prevention / control of diseases and crop disorder. New techniques of detecting mango disease are required to promote better control to avoid this crisis. By considering this, paper describes image recognition which provides cost effective and scalable disease detection technology. Paper further describes new deep learning models which give an opportunity for easy deployment of this technology. By considering a dataset of mango disease, pictures are taken from Konkan area in India. Transfer learning technique is used to train a profound Convolutionary Neural Network (CNN) to recognize 91% accuracy.
Keywords: Crop, Mango, Neural Network, Deep Learning, Image Recognition, Convolutionary Neural Network (CNN).
Scope of the Article: Deep Learning