A groundbreaking study published in Plant Phenomics on August 14, 2025, heralds a transformative advancement in the field of urban agriculture and plant biotechnology. Researchers from Kyung Hee University, led by Dae-Hyun Jung and Choon-Tak Kwon, have successfully engineered compact tomato plants tailored for the spatial constraints of vertical farming systems, presenting an innovative fusion of gene editing and artificial intelligence-based phenotyping. This research not only tackles the long-standing challenge of cultivating indeterminate fruit crops within confined indoor environments but also pioneers a scalable, non-destructive method for high-throughput plant phenotyping.
The impetus behind this work lies in the mounting pressures on the global food system, where climate change, unpredictable weather events, and rapid urbanization increasingly diminish arable land and threatening overall agricultural productivity. Vertical farming has emerged as a revolutionary agricultural approach, with its promise of dense crop production in controlled indoor environments. However, the application of vertical farming has been limited largely to leafy greens due to spatial and growth habit constraints inherent in many fruit-bearing crops like tomatoes, which typically exhibit indeterminate growth patterns unsuitable for confined cultivation spaces. Addressing this gap required a fundamental rethinking of tomato plant architecture and physiology.
Jung and Kwon’s team focused their genetic engineering efforts on the SlGA20ox gene family, known regulators of plant height through their role in gibberellin biosynthesis pathways. Through meticulous screening of twelve SlGA20ox homologs, they identified SlGA20ox2 and SlGA20ox4 as prime candidates for manipulation. Employing the revolutionary multiplex CRISPR-Cas9 system, the researchers generated both single and double knockout mutants within a triple-determinate tomato cultivar background, thereby sculpting plant stature without altering flowering time or reproductive development. This targeted gene editing effectively reduced internode length and overall plant height, rendering the tomato plants compact and more compatible with vertical farming setups.
Crucially, the altered tomato genotypes retained photosynthetic efficiency and fruit quality attributes, two parameters often compromised in growth-restricted crops. Metrics such as Fv/Fm ratios—a measure of the maximum quantum efficiency of photosystem II—chlorophyll and carotenoid content, fruit set frequency, yield per plant, fruit size, ripening kinetics, and sugar content remained statistically indistinguishable from wild-type controls. This stability was confirmed across two markedly different growing environments: traditional greenhouses and state-of-the-art vertical farming chambers. The implication is profound: compact architecture was achieved without sacrificing productivity or crop quality, a major hurdle in modern plant breeding.
Beyond the genetic modifications, the study’s innovation extends into deep learning-based phenotyping methodologies. Conventional plant phenotyping methods often lack sensitivity to subtle physiological changes and are time-consuming or destructive. The team developed an advanced 3D convolutional neural network (3D-CNN) trained on time-resolved chlorophyll fluorescence imaging data. This volumetric deep learning approach extracted intricate spatiotemporal features of chlorophyll fluorescence dynamics, capturing minute variations in photosynthetic behavior. Notably, the model excelled in discriminating between wild-type and mutant genotypes, achieving classification accuracy exceeding 84%. The 3D-CNN model outperformed conventional machine learning algorithms such as Support Vector Machines (SVM), Long Short-Term Memory Networks (LSTM), and one-dimensional CNNs, showcasing its superior generalization and feature extraction capacity.
A particularly insightful aspect was the 3D-CNN’s ability to decode genotype-specific fluorescence signatures, especially within the domain of non-photochemical quenching (NPQ). NPQ mechanisms are critical for plants to dissipate excess light energy and protect photosystems under fluctuating environmental conditions. Differences in NPQ dynamics observed through the deep learning phenotyping pipeline suggest distinct physiological adaptations conferred by SlGA20ox knockouts, offering deeper insight into the complex interplay between genetic modification and photosynthetic regulation.
These findings are poised to catalyze a paradigm shift in intelligent crop breeding strategies. By integrating next-generation CRISPR technology with sophisticated, non-invasive AI-based phenotyping, breeders can rapidly identify and select elite plants exhibiting optimal growth form and physiological function. The presented framework circumvents the need for multi-sensor, cost-prohibitive phenotyping setups, instead relying on accessible chlorophyll fluorescence imaging and scalable computational models. This is a critical step toward automating and accelerating genotype prioritization in complex breeding programs.
From an application standpoint, the development of compact, high-performance tomato cultivars with stable yields and preserved fruit quality offers an immediate boon for urban agriculture initiatives. Vertical farms, constrained by limited vertical space, can now feasibly incorporate fruit crop production alongside leafy greens, diversifying crop portfolios and enhancing food security in metropolitan centers. This aligns with a global push towards sustainable, resource-efficient food systems tailored to the realities of diminishing arable land and climate instability.
Moreover, the volumetric deep learning–based phenotyping platform holds broad potential beyond tomatoes. It can be adapted to a range of agronomically important crops, enabling breeders to non-destructively monitor physiological responses to genetic, environmental, or agronomic variables at unprecedented resolution. By capturing complex phenotypic traits underlying stress tolerance, growth habit, or yield components, this tool can drive data-driven decision-making in precision agriculture.
Funded by the National Research Foundation of Korea and supported by affiliated academic programs, this study exemplifies multidisciplinary collaboration across plant genetics, computational modeling, and agricultural engineering. The Kyung Hee University team highlights how integrating gene editing technology with artificial intelligence not only accelerates breeding cycles but also generates fundamental biological insights, expanding the frontier of plant phenomics.
In conclusion, the fusion of CRISPR-mediated genetic modification with volumetric deep learning phenotyping constitutes a pioneering approach to modern plant breeding. The compact SlGA20ox-edited tomato plants developed by Jung and Kwon’s research group represent a vital innovation for making vertical farming of fruit crops feasible on a commercial scale. Simultaneously, the robust AI-driven phenotyping pipeline offers scalable solutions for accurate, high-throughput screening of complex plant traits, heralding a new era of sustainable, smart agriculture poised to meet future global food production challenges.
As the global population continues to urbanize and climate stresses mount, technologies that enable space-efficient crop production without compromising yield or quality will become indispensable. This study not only delivers engineered solutions for the pressing spatial challenges of urban farming but also establishes a flexible analytical framework adaptable to a variety of other crop species. By marrying cutting-edge genetic tools with deep learning phenomics, researchers are charting a promising path toward resilient, efficient, and sustainable food systems worldwide.
Subject of Research: Not applicable
Article Title: Volumetric Deep Learning-Based Precision Phenotyping of Gene-Edited Tomato for Vertical Farming
News Publication Date: 14-Aug-2025
Web References: http://dx.doi.org/10.1016/j.plaphe.2025.100095
References: 10.1016/j.plaphe.2025.100095
Keywords: Plant sciences, Biochemistry, Engineering
