The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding.

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.

Soybean (Glycine max L.) is one of the most important crop plants in the Republic of Korea. Here, we conducted a soybean virome study. We harvested a total of 172 soybean leaf samples showing disease symptoms from major soybean-growing regions in the Republic of Korea. Individual samples were examined for virus infection by RT-PCR. Moreover, we generated eight libraries representing eight provinces by pooling samples and four libraries from single samples. RNA-seq followed by bioinformatics analyses revealed 10 different RNA viruses infecting soybean. The proportion of viral reads in each transcriptome ranged from 0.2 to 31.7%. Coinfection of different viruses in soybean plants was very common. There was a single dominant virus in each province, and this geographical difference might be related to the soybean seeds that transmit viruses. In this study, 32 viral genome sequences were assembled and successfully used to analyze the phylogenetic relationships and quasispecies nature of the identified RNA viruses. Moreover, RT-PCR with newly developed primers confirmed infection of the identified viruses in each library. Taken together, our soybean virome study provides a comprehensive overview of viruses infecting soybean in eight geographical regions in the Republic of Korea and four single soybean plants in detail.

The tobacco cutworm, Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae), is one of the most serious pests in field crops, vegetables, and ornamentals. Temperatures (15, 20, 25, 27, 30, 35, and 40 °C), host plants (soybean (Glycine max (L.)), maize (Zea mays L.), groundnut (Arachis hypogaea L.) and azuki bean (Vigna angularis (Willd.) Ohwi & H. Ohashi,), and the artificial diet-dependent developmental parameters and survival of S. litura were examined in this study. Stage-specific parameters such as threshold development temperature (LDT) and thermal constant (K) (Degree day (DD)) were determined by linear and nonlinear models (Sharpe–Schoolfield–Ikemoto), respectively. The total developmental time (egg–adult) decreased with increasing temperature on host plants and with an artificial diet. The total immature developmental time varied from 106.29, 107.57, 130.40, 111.82, and 103.66 days at 15 °C to 22.47, 21.25, 25.31, 18.30, and 22.50 days at 35 °C on soybean, maize, groundnut, azuki bean, and artificial diet, respectively. The LDT for the total immature completion was 7.50, 9.48, 11.44, 12.32, and 7.95 °C on soybean, maize, groundnut, azuki bean, and artificial diet, respectively. The K for the total immature completion was 587.88, 536.84, 517.45, 419.44, and 586.95 DD on soybean, maize, groundnut, azuki bean, and artificial diet, respectively. Temperature and host plant interaction also influenced the longevity and survival of adults. The findings of this study can be used to predict the number of generations, spring emergence, and population dynamics of S. litura. The nutrient content analysis of the host plants is discussed in terms of the developmental patterns of S. litura.

Maize yellow mosaic virus (MaYMV) is a tentative new Polerovirus, which was recently identified from maize (Zea mays) in China (Chen et al. 2016). MaYMV has also been reported to infect sugarcane (Saccharum spp.) and itch grass (Rottboellia cochinchinensis), and it has been reported in Asia, Africa and South America (Gonçalves et al. 2017; Palanga et al. 2017; Yahaya et al. 2017). In this study, MaYMV was detected in Panicum miliaceum and Sorghum bicolor using Illumina HiSeq2500 system by Theragen Etex Bio Institute (Suwon, Korea) and SG-VIPdb by SeqGenesis (Daejeon, Korea), as described by Lim et al. (2015). Twenty samples of P. miliaceum and sixty three samples of S. bicolor were collected from July to September and from June to October, 2016, respectively, in South Korea. The high-throughput RNA sequencing of the samples mixed into one pool resulted in a single large contig (5606-nt) with nearly complete MaYMV genome coverage. The contig was assembled from a total of 234,537 reads; the maximum, minimum...