Instance-Aware Plant Disease Detection by Utilizing Saliency Map and Self-Supervised Pre-Training

Taejoo Kim, Hyeongjun Kim, Kyeonghoon Baik and Yukyung Choi
Additional contact information
Taejoo Kim: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Hyeongjun Kim: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Kyeonghoon Baik: N.Thing Corporation, Seoul 06020, Korea
Yukyung Choi: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea

Agriculture, 2022, vol. 12, issue 8, 1-16

Abstract: Plant disease detection is essential for optimizing agricultural productivity and crop quality. With the recent advent of deep learning and large-scale plant disease datasets, many studies have shown high performance of supervised learning-based plant disease detectors. However, these studies still have limitations due to two aspects. First, labeling cost and class imbalance problems remain challenging in supervised learning-based methods. Second, plant disease datasets are either unstructured or weakly-unstructured and the shapes of leaves and diseased areas on them are variable, rendering plant disease detection even more challenging. To overcome these limitations, we propose an instance-aware unsupervised plant disease detector, which leverages normalizing flows, a visual saliency map and positional encodings. A novel way to explicitly combine these methods is the proposed model, in which the focus is on reducing background noise. In addition, to better fit the model to the plant disease detection domain and to enhance feature representation, a feature extractor is pre-trained in a self-supervised learning manner using only unlabeled data. In our extensive experiments, it is shown that the proposed approach achieves state-of-the-art performance on widely-used datasets, such as BRACOL (Weakly-unstructured) and PlantVillage (Unstructured), regardless of whether the dataset is weakly-structured or unstructured.

Keywords: computer vision; plant diseases; plant disease detection; normalizing flows; anomaly detection (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:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/8/1084/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/8/1084/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:8:p:1084-:d:869632

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().