# Herbicide Bioassay Using a Multi-Well Plate and Plant Spectral Image Analysis
## 4 Discussion
### Diagnosis of Herbicide Mode of Action Based on Spectral Response Data  
The PCA using all spectral data (RGB, CF, and IR thermal image parameters) successfully differentiated herbicides and clustered them depending on their modes of action (Figures 6 and 7). The results demonstrated a sequential separation of the tested herbicides following the order of spectral responsiveness; paraquat (PSI) was the fastest, followed by tiafenacil (PPO), glufosinate (GS), glyphosate (EPSPS), and isoxaflutole (HPPD). Penosvalum (ALS), which did not have any herbicidal activity against crabgrass in this study, was located very closely to the untreated control until the end of the measurement. The results obtained by the 3-dimensional PCA also show consistency with those by the 2-dimensional PCA, showing more distinct disparities among herbicide modes of action (Table S2, Figures S1 and S2). Our results thus demonstrate that a multi-well plate assay combined with a plant spectral image analysis can be used to diagnose and investigate herbicide modes of action. High-Throughput Screening of Herbicides Using Multi-Well Plates Assay Combined with Plant Spectral Image Analysis  
The use of multi-well plates in screening plant responses to various stresses has been reported in several previous studies [15; 16; 17]. In these studies, the CF image analysis was only used to diagnose plant responses. In our study, various plant spectral images, not only CF but also RGB and IR thermal images, were used to investigate plant responses to herbicides. To the best of our knowledge, this study was the first attempt at acquiring and analyzing three different types of spectral images for diagnosing herbicide modes of action using multi-well plates. The multi-well plate assay combined with a spectral image analysis showed a favorable synergy in the whole process of the experiment including plant preparation, image acquisition, and analysis. In comparison with the conventional whole-plant herbicide assay, our method can test many herbicidal chemicals in a limited space and time. Multiple studies using the multi-well plate assay can be simultaneously conducted in a controlled growth chamber or laboratory equipped with an image-acquisition system, enabling automatic herbicide bioassays and high-throughput screening (HTS) in herbicide discovery and development. Our method thus can be flexibly and effectively implemented into various agrochemical and plant studies, including herbicide discovery. Nevertheless, there is still much room to be improved for HTS using our method. In plant preparation, the automatic preparation of a growth medium and growing plants will save time and effort dramatically. An automatic image-acquiring system that collects continual spectral responses will save our efforts and provide a more detailed spectral understanding to diagnose herbicide modes of action. Furthermore, improving the spraying system to ensure that herbicide molecules are accurately treated in each well will increase the efficiency and speed of the multi-well plate assay in herbicide screening. Recently, deep learning algorithms have become a powerful tool for image recognition and classification and have been used in crop pests and stress diagnoses [34; 35]. A further application of deep learning to our multi-well plate assay combined with a plant spectral image analysis may improve the efficiency of discovering new herbicide molecules with new modes of action. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s24030919/s1](https://www.mdpi.com/article/10.3390/s24030919/s1)]([https://www.mdpi.com/article/10.3390/s24030919/s1](https://www.mdpi.com/article/10.3390/s24030919/s1)), Table S1. The proportion of variance that the first and second principal component (PC) explained (% of total variance). Table S2. The proportion of variance that the first, second, and third principal component (PC) explained (% of total variance). Figure S1.