Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Bulent Tugrul, Elhoucine Elfatimi and Recep Eryigit ()
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Bulent Tugrul: Department of Computer Engineering, Ankara University, Ankara 06830, Türkiye
Elhoucine Elfatimi: Department of Computer Engineering, Ankara University, Ankara 06830, Türkiye
Recep Eryigit: Department of Computer Engineering, Ankara University, Ankara 06830, Türkiye

Agriculture, 2022, vol. 12, issue 8, 1-21

Abstract: Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. 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 convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN 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 CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.

Keywords: machine learning; deep learning; plant leaf diseases (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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