As human population increases and protein demand doubles, modern plant breeders must further optimize soybean plant architecture and per plant yield for modern farming systems. Conventional techniques use imprecise visual scoring and laborious hand harvesting of single plants. Many important plant traits for modern farming systems are difficult to measure with current breeding tools, especially those related to complex physiological, structural, and environmental interactions. Attempts to accurately measure these traits often require advanced technologies or highly labor-intensive methods. Because of these challenges to future food production, UTokyo associate professor Wei Guo intends to “open a new era of artificial intelligence (AI) driven plant phenomics for these valuable but hard to access traits.

With this purpose in mind his lab teamed up with NARO soybean researcher Dr. Akito Kaga to design an image capture and AI analysis pipeline. Their technique enables much more precise and rapid measurement of single plant yield, plant architecture and seed localization with easily acquired in-field photographs or video. As Prof Wei Guo says “Most efficiency enhancing AI agricultural applications require costly aerial or robotic platforms, while our much lower cost system could be used by soybean breeders with very modest financial resources.”

UTokyo PhD candidate Tang Li developed a novel image analysis pipeline that can automatically process and estimate the number and spatial distribution of soybean seeds on a plant in the field. The deep learning image analysis pipeline, called Multi Scale Attention Network (MSAnet) uses a multi-scale attention mechanism to help count seeds. Li says “the most challenging aspect of designing MSANet was detecting only the foreground with minimal computation resources.” After focusing attention on the foreground and making seed distribution heatmaps, various tasks are conducted on upsampled images, then the images are downsampled, matched with neighboring images and a loss function is applied to increase estimate confidence. Finally, a kernel density algorithm is used to locate and count seeds, with more accurate results than any other existing pipeline. Then, easy to interpret graphs can be produced showing vertical seed distribution on individual plants that can be used by breeders to evaluate a variety of previously inaccessible traits on potential new varieties, or conduct genetic analysis on those novel traits.

Soybean breeders can use this new technique to directly select superior varieties for specific farming systems or for genetic analysis to identify the genetic regions of the soybean genome controlling vertical seed localization, plant architecture and height. According to Dr. Kaga, “MSANet will facilitate breeding for the lowest seed position, an important trait for modern machine harvesting that every breeder wants to quantify but which was previously not measurable in a high throughput pipeline”. Breeders can now rapidly identify potential new varieties with the ideal combination of traits. “I am pleased to see that the vertical seed distribution we proposed has been recognized by the breeding scientist Dr. Kaga, and I look forward to its application in real-world production,” said Li.

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References

DOI

10.34133/plantphenomics.0260

Original Source URL

https://doi.org/10.34133/plantphenomics.0260

Funding information

This work was partially supported by Ministry of Agriculture, Forestry and Fisheries (MAFF) commissioned project study on “Smart breeding technologies to Accelerate the development of new genotypes toward achieving Strategy for Sustainable Food Systems, MIDORI”, Grant Number JPJ012037, by the Japan Science and Technology Agency (JST) AIP Acceleration Research, Grant Number JPMJCR21U3 and by the Graduate School of Agricultural and Life Sciences, University of Tokyo.

Journal

Plant Phenomics