Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears

Yeong Hyeon Gu, Helin Yin, Dong Jin, Ri Zheng and Seong Joon Yoo
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Yeong Hyeon Gu: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Helin Yin: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Dong Jin: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Ri Zheng: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Seong Joon Yoo: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea

Agriculture, 2022, vol. 12, issue 2, 1-12

Abstract: Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep features extracted by deep convolutional neural networks and k -nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method.

Keywords: deep feature; fine-tuning; k -nearest neighbors; plant disease recognition; transfer learning (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 (2)

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