BATOOL ANWAR Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, Abbassia, Postal Code: 11566, Cairo,
MOHAMED M. MORSEY Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, Abbassia, Postal Code: 11566, Cairo, Egypt
ISLAM HEGAZY Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, Abbassia, Postal Code: 11566, Cairo, Egypt
ZAKI T. FAYED Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, Abbassia, Postal Code: 11566, Cairo, Egypt,
TAHA EL-ARIF Department of Computer Science, Faculty of Computer and Information Science, Ain Shams University, Abbassia, Postal Code: 11566, Cairo, Egypt

Abstract In developing countries particularly, agriculture’s essential role in the economy is indispensable, providing a large population segment with a considerable income source. Among the major crops cultivated across vast regions of the developing countries, rice stands out as a crucial staple food. However, rice crops are largely susceptible to multiple diseases, causing substantial economic losses in the agricultural sector. To mitigate these challenges, plant pathologists have been actively seeking precise and dependable methods for diagnosing rice plant diseases. Lately, machine learning has become popular in crop remote sensing applications, particularly for the classification of crop diseases. Moreover, deep learning has become a major research area in identifying crop diseases due to its ability to analyze large datasets and deliver accurate results. Keywords