Citation: Lay, L.; Lee, H.S.; Tayade, R.; Ghimire, A.; Chung, Y.S.; Yoon, Y.; Kim, Y. Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging. Plants 2023, 12, 901. https://doi.org/10.3390/ plants12040901 Academic Editor: Vittorio Rossi Received: 30 December 2022 Revised: 11 February 2023 Accepted: 14 February 2023 Published: 16 February 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). plants Article Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging Liny Lay 1,† , Hong Seok Lee 2,† , Rupesh Tayade 1,3 , Amit Ghimire 1 , Yong Suk Chung 4 , Youngnam Yoon 2 and Yoonha Kim 1,3, * 1 Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea 2 Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Republic of Korea 3 Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea 4 Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea * Correspondence: kyh1229@knu.ac.kr; Tel.: +82-53-950-5710 † These authors contributed equally to this work. Abstract: Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible. Keywords: soybean; hyperspectral imaging; spectral band; wavelength; plant disease detection 1. Introduction Soybean (Glycine max L.) is a vital commercial crop that is consumed as human food and animal feed worldwide [1–3]. However, biotic and abiotic stress factors often limit soy- bean yield [4]. According to previous reports, >100 pathogens have been reported to attack soybean plants [5,6]. In the United States alone, disease infection is accountable for ~11% annual yield losses [7]. Among the pathogens, fungi, viruses, bacteria, and nematodes are responsible for causing the highest economic loss, reducing crop yield by ~11%, ~1%, ~11%, and ~30%, respectively [8–10]. Among the various biotic stresses, the incidence of bacterial disease in soybean plants has drastically increased in recent decades [11]. In the United States, a 4–40% yield reduction was attributed to bacterial diseases [12]. Similarly, bacterial Plants 2023, 12, 901. https://doi.org/10.3390/plants12040901 https://www.mdpi.com/journal/plants