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Sugarcane Disease Detection Using Data Augmentation
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Author(s): Abhishek Verma (Centre for Advanced Studies, Lucknow, India)and Jagrati Singh (Indira Gandhi Delhi Technical University for Women, Delhi, India)
Copyright: 2024
Pages: 27
Source title:
Semantic Web Technologies and Applications in Artificial Intelligence of Things
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Amed Leyva-Mederos (Universidad Central "Marta Abreu" de Las Villas, Cuba), Sanju Tiwari (Tamaulipas Autonomous University, Mexico), Ania R. Hernandez-Quintana (Universidad de La Habana, Cuba)and Jose L. Martinez-Rodriguez (Autonomous University of Tamaulipas, Mexico)
DOI: 10.4018/979-8-3693-1487-6.ch013
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Abstract
Sugarcane is an important crop for the Indian economy, providing employment opportunities for millions of farmers. Nevertheless, the cultivation of sugarcane faces challenges from pests and diverse diseases. The detection and segmentation of plant diseases using deep learning have shown promising results in simple environments with abundant data. However, in complex environments with limited samples, the performance of existing models suffers. This study introduces an innovative method that addresses the challenges of complex environments and sample scarcity, aiming to enhance disease recognition accuracy. The highest accuracy showcased by model is 98% on testing data. Comparative study was done on the same dataset by employing various ML algorithms and achieved the highest accuracy of 70%. An Android app has been created to serve as the user interface for this model. This app enables farmers to either take pictures using their phone's camera or choose images from their gallery.
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