Plant Disease Detection Using Computational Approaches: A Systematic Literature Review

Arslan Akram; Rabia Tehseen; Shahan Yamin Siddiqui; Muhammad Farrukh Khan; Nusratullah Tauheed; Nosheen Qamar

Authors

Arslan Akram University of Central Punjab, Lahore, Pakistan
Rabia Tehseen University of Central Punjab, Lahore, Pakistan
Shahan Yamin Siddiqui Department of Computing, NASTP Institute of Information Technology, Lahore, 54000, Pakistan
Muhammad Farrukh Khan Department of Computing, NASTP Institute of Information Technology, Lahore, 54000, Pakistan
Nusratullah Tauheed Department of Computer Science, University of South Asia, Cantt Campus, Lahore 54000, Pakistan
Nosheen Qamar University of Management & Technology, Lahore, Pakistan, 53700

Keywords:

ML, DD, DL, IP

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.

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