Interpretability in the Field of Plant Disease Detection: A Review
Abstract
The early detection of diseases in plants through artificial intelligence techniques has been a very important technological advance for agriculture since, through machine learning and optimization algorithms, it has been possible to increase the yield of various crops in several countries around the world. Different researchers have focused their efforts on developing models that allow supporting the task of detecting diseases in plants as a solution to the traditional techniques used by farmers. In this systematic literature review, an analysis of the most relevant articles is presented, in which image processing techniques and machine learning were used to detect diseases by means of images of the leaves of different crops. In turn, an analysis of the interpretability and precision of these methods is carried out, considering each phase of the image processing, segmentation, feature extraction and learning processes of each model. In this way, there is evidence of a void in the field of interpretability since the authors have focused mainly on obtaining good results in their models, beyond providing the user with a clear explanation of the characteristics of the model.
Keywords
Machine Learning, Classification, Early detection of diseases, Interpretability, Image processing
Author Biography
Daniel-David Leal-Lara
Roles: Conceptualization, Research, Content - Curation, Methodology, Writing - Original draft, Writing – Review & editing.
Julio Barón-Velandia
Roles: Conceptualization, Methodology, Writing - Original draft, Writing – Review & editing.
Camilo-Enrique Rocha-Calderón
Roles: Validation, Writing – Review & editing.
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