The lack of understanding of topics in uncharted research areas can often be mitigated through a careful review of the existing literature. However, when a field is not well-studied, relying on assumptions before starting a project should be avoided. This article highlights the dangers of such presumptions as demonstrated by the case of brown planthopper (Nilaparvata lugens) detection in rice field. Although unpiloted aerial vehicles (UAVs) have shown promise in various agricultural applications, their effectiveness in the early detection of brown planthopper damage was initially assumed based on the expectation of visible symptoms. The image analysis in the current study indicated that images obtained from a camera mounted on a UAV could not detect the symptoms of the very early stages of damage from brown planthoppers. An overlooked factor was whether the pest damage was uniformly distributed across an entire rice plant. If symptoms appear consistently, early detection using a top-down view from a UAV is possible; otherwise, detection may be delayed. Our findings emphasize the need for thorough preliminary research to avoid failure. By investigating the biological characteristics of the target pest and the potential limitations of detection methods, researchers can greatly improve their chances of success. We hope that readers will recognize the importance of thoroughly examining unexplored areas before embarking on new research.

Image-based digitalization of germplasm stock holds significant promise for accelerating plant breeding and crop improvement. This technology facilitates efficient germplasm characterization, evaluation, and management through the capture and analysis of visual phenotypes. However, widespread adoption is hindered by challenges that include image quality control, data analysis complexity, and phenotypic representation limitations. This study investigated these constraints and proposed strategies to address them. By managing technical challenges, refining phenotypic data extraction, and developing robust data analysis pipelines, researchers can fully leverage image-based digitalization to enhance germplasm utilization and contribute to sustainable agriculture.

Low-temperature damage at the seedling stage is one of the most significant natural obstacles to wheat’s growth. In domestic wheat breeding programs, the selection of cold-tolerant varieties is crucial for the development of superior wheat varieties. Traditionally, the extent of damage caused by freezing wheat is estimated through visual observation. In this study, we compared the RGB image analysis method with conventional visual evaluation and chlorophyll content analysis methods to determine if this method could accurately quantify the cold tolerance discrimination of wheat in the field. First, single-leaf-level RGB image analysis revealed a pattern similar to dead leaf ratio and chlorophyll content in three grades of freezing injury. Next, we compared the significance of plant-level RGB image analysis. The greenness index by RGB image analysis showed a higher correlation with dead leaf ratio by visual evaluation. Finally, 40 wheat varieties were planted in the field and wheat canopy images were collected at the seedling stage after wintering. There was a high correlation between the greenness index and the visual evaluation. However, there was no correlation between dead leaf ratio and visual evaluation or greenness index as determined by RGB image analysis. These findings suggest that using RGB image analysis rather than visual evaluation can be useful in assessing freeze damage in wheat fields.

To improve the seed purity management system of Korean wheat cultivars, 50 Korean wheat cultivars were subjected to chemical assays for grain color, genotyping of grain weight-related genes, and grain image analysis. The tested cultivars were primarily classified by NaOH and ninhydrin tests as white (26%) and red (74%) cultivars, as well as high PPO activity (48%), and low PPO activity (52%) cultivars, respectively. The allelic variations of Tamyb10 gene revealed Tamyb-A1a/Tamyb-B1a/Tamyb-D1a as the major allelic combination in white wheat and five different Tamyb10 genotypes (i.e., aba, abb, baa, bba, and bbb) in red wheat. Those cultivars with high PPO activity possessed the Ppo-A1a/Ppo-B1b/Ppo-D1b genotype, while those with low PPO activity possessed the Ppo-A1b/Ppo-B1a/Ppo-D1a genotype. In the grain image analysis, long grain cultivars displayed increased grain width, circularity, and area. Based on cluster analysis of grain traits, the Korean wheat cultivars were classified into two groups - 1) large red grain cultivars released before 2000, and 2) small red grain cultivars and white wheat cultivars released after 2000. Further research is required to determine the effects of grain filling conditions on the grain characteristics of Korean wheat cultivars and to develop efficient and reliable molecular markers for an improved seed purity management system.

Fast and accurate selection is essential for breeding to cope with rapid climate changes and a steeply increasing population. Consequently, technologies for high-throughput phenotyping (HTP) are emerging. These technologies, unlike conventional phenotyping methods, enable us to evaluate agronomic traits in a fast and massive manner. Thus, the HTP facility was built to acquire and analyze crop images using RGB sensors at the National Institute of Agricultural Sciences, Republic of Korea. By testing various conditions to acquire images, we determined the conditions for phenotyping using the RGB sensor as follows: exposure 30,000 ms, gamma 75, and gain 100 using LED lights in a blue background. Based on this condition, images from 96 individual plants of rice Dongjin cultivar were obtained every week to measure plant height and shoot area, which are directly associated with yield. The results obtained from the image analysis were compared with the manually collected results. The r² value between the projected plant height obtained from image analysis and the plant height obtained from manual measurement was 0.989. Furthermore, the r² value between the projected shoot area obtained from image analysis and the shoot area obtained from manual measurement was 0.981. These results show that image analysis is highly reliable and can be used for crop phenotyping. Therefore, we expect that the new method we developed will be used for breeding in the near future.