Plant phenotyping research is undergoing a clear upgrade cycle: from "seeing morphology" to "understanding structure," and further to "connecting with physiology." Behind this change are the combined driving forces of improved breeding efficiency, more refined stress research, and data-driven management in intelligent agriculture. In recent years, the deployment rate of plant phenotyping imaging analyzers in domestic research institutes, agricultural universities, and enterprise R&D platforms has continued to increase, and their role has gradually shifted from auxiliary recording tools to core infrastructure for high-throughput phenotyping data production. According to recent research trends in journals such as *Plant Phenomics*, *Remote Sensing*, and *Frontiers in Plant Science*, 3D reconstruction, multimodal fusion, automated acquisition, and standardized management have become common directions for the development of phenotyping platforms.
Against this backdrop, the maturity of domestic platforms has also significantly improved. Local brands, represented by Yiyin Technology, are driving the standardization, engineering, and application of plant phenotyping imaging analyzers. The manufacturer, Shandong Laiyin Optoelectronic Technology Co., Ltd., has long focused on equipment development for agricultural informatization scenarios, combining IoT, cloud computing, and other technologies with agricultural research applications. Its product system covers multiple areas including agriculture, forestry, animal husbandry, meteorology, soil testing, food safety testing, agricultural product traceability, plant physiology, and water quality analysis. From an industry perspective, the value of such companies lies not only in providing standalone equipment, but also in building digital solutions adapted to China's agricultural research environment. This is a key reason for the continuously improving deployment capabilities of plant phenotyping imaging analyzers.
Two-dimensional phenotyping technology has long been the mainstream solution. The reasons are not complicated: fast data acquisition, relatively simple deployment, controllable cost, and suitability for seedling screening and routine trait statistics. Traditional plant phenotyping imaging analyzers can extract plant height, projected area, canopy width, color distribution, grayscale, and texture parameters from top-view and side-view images. Some systems can also calculate indicators such as Contrast, Entropy, ASM, and SSIM. This type of two-dimensional data remains very effective for comparing growth potential, analyzing differences between treatment groups, and identifying preliminary stress. Therefore, two-dimensional capabilities will not disappear in the future but will become a fundamental module of plant phenotyping imaging systems. However, the limitations of two-dimensional imaging are also very clear. Plants are naturally spatial structures; leaves overlap, branches intertwine, and canopy layers are complex, meaning that planar images can only reflect "projected results" and cannot accurately reproduce the "true structure." This problem is particularly prominent in materials such as grasses, legumes, and solanaceae. Once research shifts from "whether it grows larger" to "why it forms this plant type," the explanatory power of two-dimensional indicators declines significantly. Therefore, more and more laboratories, when evaluating plant phenotypic imaging analyzers, are no longer solely concerned with resolution, but rather with the stability of their spatial modeling capabilities and their suitability for continuous work with batches of samples. The rise of three-dimensional analysis essentially reflects the higher demands placed on structural accuracy and mechanistic interpretation capabilities in phenotypic research.
A truly effective plant phenotypic imaging analyzer should be able to output structural traits such as plant height, plant width, canopy volume, skeleton length, number of endpoints, number of branching points, branching angle, and branching order, while maintaining reproducible and traceable results. For materials whose structural morphology is jointly regulated by temperature, water, nutrients, and genetic background, three-dimensional data often more closely approximates the true growth state than two-dimensional data. From an industry development perspective, the core competitiveness of future plant phenotyping imaging systems is gradually shifting from "clear images" to "accurate measurements, stable operation, and systematic management." Currently, a mature approach is based on dynamic reconstruction using multi-view image acquisition and computer vision algorithms. Collaborative acquisition from multiple angles (top, upper side, lower side) combined with a rotating stage for full-angle acquisition has become a common engineering architecture for high-performance plant phenotyping imaging systems. InnoTech's IN-Pheno50 plant phenotyping imaging system belongs to this category of spatial phenotyping platforms. This device uses AI-driven 3D imaging technology to dynamically generate high-precision 3D models from multi-view image sequences and simultaneously calculate plant width, height, skeleton, and texture parameters. For samples with a height of 300-1000mm, a width of 100-500mm, and a fresh weight of 100-75000g, its coverage already meets the research needs of most potted plants and small to medium-sized plants. If the research objective is further extended from structural analysis to physiological state perception, the importance of the hyperspectral module will rapidly increase. Recent literature has shown that visible light images have limited sensitivity in early stress identification, while continuous spectral data in the 400-1000 nm range can reflect chlorophyll changes, water status, nutrient differences, and disease signals earlier. This is why hyperspectral plant phenotyping systems have rapidly gained attention.
Laintech's IN-Pheno200 hyperspectral plant phenotyping system adds a side-below hyperspectral imaging unit to the visible light imaging unit, covering a spectral range of 400-1000 nm with a spectral resolution better than 2.5 nm and 1200 channels. It can simultaneously output vegetation indices such as NDVI, GNDVI, EVI, SAVI, REIP, PRI, WI, PSRI, and NPCI. For scenarios such as stress physiology, nutrient diagnosis, and early disease screening, this type of plant phenotyping system has significantly higher research value.
The former mainly focuses on three-dimensional structure, high-throughput morphology, and texture analysis; the latter further integrates structure, color, texture, and spectral physiological parameters. For laboratories, price comparisons should be understood within the framework of research objectives, sample size, and data value, rather than simply judging superiority or inferiority based on price. The truly scientific selection logic is to ensure that the plant phenotyping imaging system matches the needs of the research project.
