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Plant disease control and idenƟficaƟon are essenƟal in agricultural seƫngs to maintain crop health and yield. This paper suggests a
novel method for detecƟng mango leaf illness that combines the capabiliƟes of the Random Forest classifier with the deep
convoluƟonal neural network (CNN) ResNet50. Mango leaf photos may be effecƟvely used to extract characterisƟcs related to diseases
and complicated paƩ erns by uƟlizing the feature extracƟon capabiliƟes of ResNet50, which has been pre-trained on large-scale image
datasets. These characterisƟcs are then used by the robust and interpretable Random Forest classifier to idenƟfy and categorize
different mango leaf diseases. This method provides a comprehensive soluƟon for the accurate and efficient diagnosis of mango leaf
disease through the integraƟon of Random Forest and ResNet50. It also gives farmers and agricultural pracƟƟoners Ɵmely informaƟon
for managing diseases and protecƟng crops.
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