Abstract
Citrus yield decreases are mostly caused by citrus fruit and leaf diseases. As a result, developing an automated detection method for citrus plant diseases is critical. Deep learning algorithms have recently shown promising and favourable outcomes, prompting us to take on the problem of identifying citrus fruit and leaf illnesses. MLP classifiers are suggested using a unified approach in this paper. The suggested Multilayer perceptron classifier model is focused on distinguishing healthy fruits and leaves from those with common citrus illnesses such as Scab, Melanose, canker, Black spot, and greening disease. The preliminary findings show that the MLP classifier model outperforms the other model by a significant margin.
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Anandamurugan, S., Deva Dharshini, B., Ayesha Howla, J., Ranjith, T. (2022). Deep Neural Network Model for Automatic Detection of Citrus Fruit and Leaf Disease. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_32
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DOI: https://doi.org/10.1007/978-3-030-96299-9_32
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