@ARTICLE{Kavitha_K_J_CNN_2024,
 author={Kavitha, K J and Krishna Prasad, K},
 volume={vol. 70},
 number={No 2},
 journal={International Journal of Electronics and Telecommunications},
 pages={455-464},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision.},
 type={Article},
 title={CNN ensemble approach for early detection of sugarcane diseases – a comparison},
 URL={http://journals.pan.pl/Content/131807/26-4432-Kavitha-sk.pdf},
 doi={10.24425/ijet.2024.149566},
 keywords={Sugarcane diseases, CNN, KNN, Soil quality, quality metrics},
}