Open AccessDisease Identification in Chilli Leaves using Machine Learning Techniques
Author(s) -
Sufola Das Chagas Silva E Araujo,
V. S. Malemath,
K. Meenakshi Sundaram
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1061.1291s319
Subject(s) - convolutional neural network , artificial intelligence , machine learning , feature extraction , computer science , artificial neural network , deep learning , identification (biology) , android application , pattern recognition (psychology) , android (operating system) , biology , botany , operating system
Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms. The model uses an object detection algorithm and supervised image recognition and feature extraction using convolutional neural network to classify crops as infected or healthy. Google machine learning libraries, TensorFlow and Keras are used to build neural network models. An Android application is developed around the model for the ease of using the disease detection system.