Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Jinzhu Lu, Lijuan Tan and Huanyu Jiang
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Jinzhu Lu: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
Lijuan Tan: Modern Agricultural Equipment Research Institute, Xihua University, Chengdu 610039, China
Huanyu Jiang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2021, vol. 11, issue 8, 1-18

Abstract: Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

Keywords: plant disease classification; deep learning; machine learning; convolutional neural network (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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