Abstract
Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.
Highlights
Plants are often attacked by various pathogens during their growth and development (Suzuki et al, 2014), resulting in abnormal physiological and morphological changes in plants
The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance
We mainly focused on the following image sets from PlantVillage: (1) three types of leaf diseases on tomatoes (Figure 3C), such as bacterial spot of tomato (Adhikari et al, 2020), tomato leaf mold (Rivas and Thomas, 2005), and tomato yellow leaf curl virus (TYLCV) (Prasad et al, 2020); (2) cherry powdery mildew (Gupta et al, 2017; Figure 3B); (3) leaf scorch of strawberry (Dhanvantari, 1967; Figure 3A)
Summary
Plants are often attacked by various pathogens (e.g., bacteria, viruses, fungi, etc.) during their growth and development (Suzuki et al, 2014), resulting in abnormal physiological and morphological changes in plants. In severe cases, it may disrupt its normal growth and development and even cause large-scale disasters, such as leaf spot disease (Ozguven and Adem, 2019), powdery mildew (Lin et al, 2019), brown spot and blast diseases (Phadikar and Goswami, 2016), and gray mold (Fahrentrapp et al, 2019). To solve the above problems, many researchers made great efforts on the diagnosis of plant diseases by exploring the relationship between pathogen infection and plant disease
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
