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Liu, Z.Y., Shi, J.J., Zhang, L.W. and Huang, J.F. (2010) Discrimination of Rice Panicles by Hyperspectral Reflectance Data Based on Principal Component Analysis and Support Vector Classification. Journal of Zhejiang University Science B, 11, 71-78.
https://doi.org/10.1631/jzus.B0900193

has been cited by the following article:

• TITLE: An Efficient Disease Detection Technique of Rice Leaf Using AlexNet

  AUTHORS: Md. Mafiul Hasan Matin, Amina Khatun, Md. Golam Moazzam, Mohammad Shorif Uddin

  KEYWORDS: AlexNet, Leaf Diseases, Disease Prediction, Rice Leaf Disease Dataset, Disease Classification

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.12, December 14, 2020

  ABSTRACT: As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.