Plant Disease Detection Using Computational Approaches: A Systematic Literature Review

Abstract

Rapid improvements in ML and DL techniques have made it possible to detect and recognize objects from images. Computational approaches using ML and DL have been recently applied to agriculture or farming applications and are proving successful in increasing per-yield production. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use DL and ML approaches to address various plant DD concerns was undertaken in the current publication. In this work, we have reviewed 35 of the most recent DL and ML-based articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the ML and DL used in plant leaf DD. Moreover, DCNN trained on image data was the most effective method for detecting early DD. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant DD.

Code and data availability

This is a systematic literature review of plant disease detection using CNN/ML/image processing. It presents no original phenotype datasets, plant images, author code, models, or supplements; all cited datasets (e.g., PlantVillage, FieldPlant) belong to prior work, and no availability statements or author URLs for the

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