HIGHLIGHTS
SUMMARY
Lee et_al proved that if a model was trained with datasets containing plant diseases that were not associated with a specific crop, the model would be more suitable for a wider range of uses, especially for images obtained in different fields and images from unseen crops. Efforts have been made to develop alternative techniques, including image recognition based on machine_learning for its timely feedback and low cost (Coulibaly et_al, 2019; Abade et_al, 2021; Bari et_al, 2021). Early automatic diagnoses of crop diseases were mainly done via image recognition based on traditional machine_learning (Li et_al . . .
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