Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence

Muhammad Owais; Ehtesham Iqbal; Samee Ullah Khan; Muhammad Umraiz; Yusra Abdulrahman; Irfan Hussain

cs.CVSep 28, 2026

Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence

Authors: Muhammad Owais, Ehtesham Iqbal, Samee Ullah Khan, Muhammad Umraiz, Yusra Abdulrahman, Irfan Hussain

Organizations: Khalifa University Center for Autonomous Robotic Systems (KU-CARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. · Advanced Research and Innovation Center (ARIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates. · Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea. · Department of Aerospace Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

Abstract

This review examines the evolution of plant phenotyping from conventional manual trait measurement to high-throughput, robotic, and artificial intelligence-driven crop monitoring. Despite significant advances in imaging, autonomous platforms, multimodal sensing, and deep learning, current phenotyping systems remain fragmented across sensing modalities, crop traits, growth stages, environments, and management objectives. We therefore frame phenotyping as an integrated \emph{seed-soil-plant-environment-management} (SSPEM) intelligence problem, where crop performance reflects interactions among seed quality, root-zone conditions, plant development, environmental exposure, and management actions. The review synthesizes conventional, high-throughput, robotic, and AI-driven phenotyping approaches, highlighting their capabilities and persistent limitations in temporal integration, multimodal reasoning, biological interpretation, and actionable decision support. Building on this analysis, we introduce a conceptual PhenoAgent framework that extends phenotyping beyond the estimation of isolated traits to evidence-based crop-state interpretation, uncertainty-aware reasoning, and management-oriented support. The PhenoAgent concept primarily brings together scattered advances in phenotyping to deliver insights ranging from detailed to high-level, such as what is happening in the crop, why it might be occurring, what evidence is missing, and what actions or additional measurements should be considered. We also discuss challenges in dataset scarcity, annotation, benchmarking, model generalization, and explainability. By linking multimodal phenotyping with agentic AI and closed-loop decision support, this review outlines a path to interpretable, scalable, and deployment-oriented crop intelligence.

Figures & tables

Figure 1: Conceptual framework for AI-driven precision agriculture integrating multimodal visual sensing with IoT and environmental data. AI models and large language models (LLMs) support crop detection, ripeness estimation, and disease monitoring, producing spatial ripeness maps and decision support for harvest scheduling, resource allocation, and revenue optimization.

Figure 2: Conceptual architecture of Pheno Agent for integrative phenotyping and decision support. Temporal fusion and digital twins support the integration of multimodal data across seed traits, soil and root-zone properties, plant growth and physiology, environmental conditions, and management practices. The framework combines AI reasoning, domain knowledge, and models with explainable AI and uncertainty quantification to support interpretable decisions.

Review	Focus	PP	SP	RZ	Rob	DL	FM	DT	AR	CLA	Limitations
Pieruschka and Schurr [ 73 ]	History, infrastructure, standardization, and future needs in plant phenotyping	✓	⚫	⚫	⚫	⚫	✗	✗	✗	✗	Does not focus on agentic AI, foundation models, or closed loop seed soil plant reasoning.
Murphy et al. [ 64 ]	Deep learning in image based plant phenotyping	✓	✗	✗	⚫	✓	⚫	✗	✗	✗	Strong AI review, but primarily image based and not organized around multimodal decision intelligence.
Jin et al. [ 37 ]	Deep learning for high throughput seed phenotyping	⚫	✓	✗	✗	✓	✗	✗	✗	✗	Seed specific focus, with limited connection to downstream plant, environment, management, and intervention decisions.
Wang et al. [ 102 ]	Image based high throughput phenotyping technologies and trends	✓	⚫	⚫	✓	✓	⚫	⚫	✗	⚫	Broad technology synthesis, but agentic reasoning and unified management intelligence are not central.
Zhang et al. [ 111 ]	Ground mobile robots for high throughput plant phenotyping from perception decision action perspective	✓	✗	⚫	✓	✓	⚫	✗	⚫	✓	Strong robotic pipeline focus, but less emphasis on seed soil plant digital twins and foundation agent reasoning.
Li et al. [ 49 ]	Foundation models in smart agriculture	⚫	✗	⚫	⚫	✓	✓	⚫	⚫	⚫	Broad smart agriculture focus, not specifically centered on plant phenotyping workflows and phenotypic validation.

Table 1: Comparison of closely related AI-driven plant phenotyping review papers with our conceptual PhenoAgent review. ✓ = addressed in detail; ⚫ = partially addressed; ✗ = not a central focus. PP = plant phenotyping; SP = seed phenotyping; RZ = root zone; Rob. = robotics; DL = deep learning; FM = foundation models; DT = digital twins; AR = agentic reasoning; CLA = closed-loop action. The comparison highlights that existing reviews strongly cover sensors, imaging, robotics, and deep learning, while a unified agentic seed-soil-plant-environment-management (SSPEM) intelligence framework remains underdeveloped.

Figure 3: Literature selection PRISMA diagram illustrating the identification, screening, eligibility assessment, and inclusion stages. Of the 318 records initially identified, 246 underwent screening, 160 were assessed for eligibility, and 114 were included in the final review.

RQ	Research question	Review contribution
RQ1	How has plant phenotyping evolved from manual measurement to AI enabled automation?	Provides a compact historical synthesis of conventional phenotyping, high throughput sensing, robotic platforms, and AI driven trait extraction.
RQ2	Which current technologies enable automated phenotyping?	Compares imaging, IoT, UAV, UGV, robotic, deep learning, foundation model, multimodal fusion, digital twin, and dashboard advances.
RQ3	Where are current systems still fragmented?	Identifies gaps in dataset standardization, multimodal fusion, temporal reasoning, biological interpretability, and seed soil plant integration.
RQ4	How can PhenoAgent define the next generation of phenotyping?	Introduces a conceptual framework for agentic (SSPEM) intelligence with explanation, forecasting, and intervention recommendation.
RQ5	What is needed for high TRL translation?	Discusses benchmarking, validation, edge/cloud deployment, human oversight, data ownership, commercialization, and operational adoption.

Table 2: Research questions and corresponding contributions of the proposed review.

Figure 4: Conventional plant phenotyping workflow and its limitations. Sequential stages span seed lot testing, germination assays, soil/substrate sampling, greenhouse trial establishment, manual measurements, destructive harvest, yield assessment, and statistical analysis. Highlighted limitations include limited sample representativeness, unobserved spatial and temporal variability, unquantified establishment losses, and the loss of intermediate growth trajectories. Endpoint-focused assessments and delayed analysis constrain the integration of seed, soil, and plant responses and limit feedback for early decision-making.

Phenotyping layer	Typical conventional measurements	Common methods	Strengths	Key limitations	References
Seed quality and vigor	Viability, germination percentage, germination speed, seedling vigor, aging tolerance, stress germination	Standard germination test, accelerated aging, electrical conductivity, tetrazolium staining, seedling length and dry weight	Directly linked to crop establishment and seed lot value	Time consuming, often destructive or semi destructive, limited single seed tracking, weak field prediction under stress	Rajjou et al. [ 80 ] ; Marcos Filho [ 57 ] ; Reed et al. [ 82 ] ; Xing et al. [ 107 ]
Soil/substrate and root zone	Moisture, pH, electrical conductivity, salinity, nutrient availability, bulk density, organic matter, microbial or rhizosphere indicators	Soil sampling, laboratory chemical analysis, pot/substrate assays, root washing, soil cores, trenching	Provides mechanistic context for plant performance	Spatially sparse, delayed lab turnaround, destructive sampling, poor linkage to individual plant time series	Viscarra Rossel et al. [ 99 ] ; Bünemann et al. [ 10 ] ; de la Fuente Cantó et al. [ 18 ] ; Blanchy et al. [ 7 ]
Root architecture	Root depth, root angle, root length density, root biomass, crown root number, lateral roots, root hair traits	Excavation, shovelomics, root washing, rhizoboxes, minirhizotrons, root imaging after harvest	Captures belowground traits relevant to water and nutrient uptake	Laborious, genotype by soil sensitivity, disturbance of rhizosphere, low throughput in field plots	Trachsel et al. [ 94 ] ; Kuijken et al. [ 45 ] ; Atkinson et al. [ 6 ] ; Takahashi and Pradal [ 88 ]
Shoot growth and canopy traits	Plant height, leaf area, biomass, tillering, canopy cover, chlorophyll, flowering time, plant architecture	Rulers, calipers, SPAD meters, visual scoring, leaf area meters, destructive biomass harvest	Simple, interpretable, widely used in breeding and agronomy	Observer bias, sparse temporal resolution, destructive endpoints, limited spatial coverage	Fiorani and Schurr [ 26 ] ; Araus and Cairns [ 2 ] ; Watt et al. [ 103 ] ; Wang et al. [ 102 ]
Stress and disease response	Wilting, chlorosis, necrosis, disease severity, drought response, salinity response, nutrient deficiency symptoms	Visual scales, manual scoring, tissue sampling, physiological assays, yield loss assessment	Agronomically meaningful and accepted by breeders	Subjective scoring, late symptom visibility, low repeatability among observers, limited early warning ability	Poorter et al. [ 74 ] ; Tardieu et al. [ 89 ] ; Murphy et al. [ 64 ]
Yield and product quality	Grain yield, fruit number, fruit weight, harvest index, quality grades, postharvest traits	Manual harvest, combine yield, laboratory quality testing, grading	Final economic endpoint and breeding target	End of season only, confounded by many earlier processes, weak diagnostic value for cause of failure	Ray et al. [ 81 ] ; Cooper et al. [ 15 ] ; Mahmood et al. [ 56 ]

Table 3: Conventional phenotyping measurements across the seed soil plant continuum. The table shows why these methods remain valuable biological references, but are inadequate for continuous, scalable, decision-oriented phenotyping.

Year	Study	Domain	Contribution	Conventional relevance	Limitations	Relevance to PhenoAgent
2011	Furbank and Tester [ 28 ]	Plant phenomics	Framed phenotyping as a bottleneck limiting crop improvement	Established need for higher throughput measurement	Did not yet address modern agentic or multimodal reasoning	Provides historical motivation for automated intelligence
2011	Trachsel et al. [ 94 ]	Root phenotyping	Introduced shovelomics for field root architecture assessment	Practical method for root crown traits in field breeding	Destructive, labor intensive, limited temporal monitoring	Shows why belowground sensing must become less destructive
2012	Rajjou et al. [ 80 ]	Seed germination/vigor	Reviewed molecular and physiological bases of germination vigor	Connects seed vigor to establishment success	Molecular markers are not automatically linked to later crop management	Supports seed to plant phenotype continuity
2013	Fiorani and Schurr [ 26 ]	Plant phenotyping	Discussed future scenarios for noninvasive phenotyping	Emphasized resource use traits and environmental response	Large scale data integration remained a challenge	Anticipates the need for integrated phenotyping systems
2014	Araus and Cairns [ 2 ]	Field phenotyping	Positioned field HTPP as a crop breeding frontier	Linked phenotyping to breeding under realistic environments	Field data remain noisy and difficult to interpret causally	Motivates context aware phenotyping agents
2015	Marcos Filho [ 57 ]	Seed vigor	Reviewed seed vigor testing development and practice	Clarifies why vigor is more informative than germination alone	Many tests remain species dependent and laborious	Supports seed quality reasoning within PhenoAgent

Table 4: Chronological development of conventional and transitional studies relevant to seed, soil/root zone, plant growth, field trail, and G

\times

\times

M phenotyping. The ordering highlights how the field moved from recognizing the phenotyping bottleneck toward integrated but still incomplete seed soil plant assessment.

Figure 5: Dynamic genotype environment management (GEM) interactions shaping plant phenotypes over time. Genetic potential, environmental exposure, and management practices jointly influence root development, canopy growth, stress response, yield formation, and quality. The pathway highlights the progression from seed vigor and establishment to vegetative growth, stress adaptation, reproductive development, and final crop outcome, emphasizing the need for time-resolved phenotyping to interpret measured traits.

Limitation	How it appears in conventional phenotyping	Scientific consequence	Operational consequence	PhenoAgent opportunity
Labor intensity	Manual scoring, plant measurement, seedling counting, root washing, destructive harvest, soil sampling	Limits population size, trait diversity, and temporal frequency	Increases cost and reduces commercial scalability	Automate observation using sensors, robots, and AI assisted annotation
Destructive endpoints	Biomass harvest, root excavation, tissue analysis, seedling dissection	Prevents continuous tracking of the same plant or plot	Delays understanding of stress progression and recovery	Link non-destructive sensing with selective ground-truth sampling
Subjectivity	Visual vigor, disease severity, wilting, lodging, chlorosis and canopy scores	Reduces reproducibility and introduces observer bias	Limits trust and comparability across sites and operators	Use calibrated models with uncertainty estimates and expert review
Sparse temporal data	Measurements collected weekly, at key stages, or only at harvest	Misses transient stress, early divergence, and treatment-response timing	Prevents timely intervention in greenhouse and field trials	Build temporal digital plant records and growth trajectories
Separated data streams	Seed, soil, weather, irrigation, image, and yield data stored independently	Weak causal interpretation across the seed soil plant continuum	Makes trial decisions reactive rather than predictive	Fuse seed, soil/root zone, plant, environment, and management data
Weak G $\times$ E $\times$ M coverage	Limited factorial combinations and few environments	Important interactions remain hidden or confounded	Slows breeding and management optimization	Use agentic reasoning to prioritize measurements, treatments, and interventions

Table 5: Limitations of conventional phenotyping and their implications for PhenoAgent based next generation phenotyping.

Figure 6: Current high-throughput phenotyping ecosystem. The framework summarizes the main sensing modalities, data-acquisition platforms, AI methods, and application outputs used for automated crop monitoring, including trait extraction, stress assessment, yield prediction, and decision dashboards. It also highlights the remaining gap between measurement-driven outputs and biologically grounded crop-state reasoning.

Modality	Traits / Signals	Deployment	Strength	Current limitation	References
RGB imaging	Emergence, leaf area, canopy cover, color, organ count, lesions, fruit count, growth rate	Fixed cameras, UGVs, UAVs, smartphones, greenhouse stations	Low cost, high spatial detail, compatible with deep learning	Sensitive to illumination, occlusion, background, and visible-symptom delay	Minervini et al. [ 60 ] ; Pound et al. [ 75 ] ; Tausen et al. [ 90 ] ; Meraj et al. [ 59 ]
Thermal imaging	Canopy temperature, water stress, transpiration proxy, irrigation response	UAVs, fixed greenhouse cameras, gantry systems	Enables early water-status monitoring beyond RGB appearance	Requires calibration and correction for weather, angle, and emissivity	Perich et al. [ 72 ] ; Xie and Li [ 108 ] ; Nguyen et al. [ 65 ]
Multispectral imaging	Vegetation indices, chlorophyll, biomass proxy, nitrogen, stress indices	UAVs, handheld sensors, greenhouse cameras	Balances cost and physiological sensitivity	Band selection and vegetation-index saturation can limit generality	Sankaran et al. [ 84 ] ; Xie and Li [ 108 ] ; Khuimphukhieo and Silva [ 42 ]
Hyperspectral imaging	Pigment, water, nitrogen, disease, salinity and biochemical fingerprints	UAVs, proximal carts, laboratory and greenhouse imaging	Dense spectral information for subtle stress and composition changes	High data volume, calibration burden, limited interpretability without models	Nguyen et al. [ 65 ] ; de Silva and Brown [ 19 ] ; Wang et al. [ 102 ]
Depth and LiDAR	Plant height, canopy volume, row structure, lodging, 3D architecture, biomass proxy	UAV LiDAR, UGV LiDAR, gantry systems, RGB-D cameras	Adds spatial structure and geometry, reducing ambiguity of 2D images	Cost, registration, occlusion, point-cloud processing, and cross-platform calibration	Busemeyer et al. [ 11 ] ; Xie and Li [ 108 ] ; Nguyen et al. [ 65 ]
IoT and environmental sensors	Temperature, humidity, light, CO2, substrate moisture, pH, EC, irrigation, fertigation, actuator logs	Smart greenhouses, vertical farms, field sensor networks	Provides continuous causal context for plant response	Often stored separately from image-derived traits and biological labels	Wolfert et al. [ 105 ] ; Peladarinos et al. [ 71 ] ; Rahman et al. [ 79 ]

Table 6: Key sensing modalities employed in today’s high-throughput phenotyping and how they relate to seed-soil-plant intelligence. The table highlights that while each modality provides useful information, none is sufficient on its own when considered in isolation.

Figure 7: Platform-level comparison of fixed cameras, UAVs, UGVs, robotic gantries, and smart greenhouses. The comparison highlights their complementary strengths and limitations in spatial coverage, temporal frequency, sensing resolution, mobility, environmental context, and potential for closed-loop crop management.

Year	Study	Domain	Main advance	Contribution	Remaining gap	Relevance to PhenoAgent
2011	Furbank and Tester [ 28 ]	Phenomics vision	Defined phenotyping bottleneck	Motivated sensor-based scale-up	Limited AI and robotics integration	Historical baseline for automation
2012	White et al. [ 104 ]	Field phenomics	Framed field-based phenomics for genetics	Linked field measurements to genetic discovery	Sparse automation compared with current systems	Supports field-relevant validation
2013	Busemeyer et al. [ 11 ]	Multi-sensor platform	Tractor-based BreedVision system	Combined sensors for field breeding	Heavy platform, limited reasoning layer	Demonstrates platform-level integration
2013	Cobb et al. [ 14 ]	Crop improvement	Defined next generation phenotyping requirements	Connected phenotyping to genotype-phenotype understanding	Data integration remained unresolved	Supports biological interpretation focus
2014	Andrade-Sanchez et al. [ 1 ]	Field robotics	Field-based high throughput phenotyping platform	Enabled repeated crop measurements in field plots	Platform-specific workflow	Motivates mobile phenotyping infrastructure
2014	Araus and Cairns [ 2 ]	Field HTPP	Positioned field phenotyping as breeding frontier	Linked sensing to selection under real environments	Translating data to knowledge remained hard	Supports breeding-facing PhenoAgent

Table 7: Chronological map of representative advances in high throughput, robotic, AI driven, multimodal, and digital twin phenotyping. The studies are ordered by year to show how the field evolved from platform construction and image analysis toward deep learning, fusion, foundation models, and decision oriented digital systems.

Current Capability	What current systems do well	Main unresolved gap	Design requirement for PhenoAgent	Supporting literature
High throughput sensing	Rapid imaging, repeated measurements, spectral and structural traits	Sensor outputs remain fragmented by modality and platform	Time-synchronized sensor fusion by plant, plot, treatment, and growth stage	Tardieu et al. [ 89 ] ; Wang et al. [ 102 ]
Robotic and UAV platforms	Scalable field or greenhouse data acquisition	Limited biological interpretation and closed loop action	Platform-aware sensing plans and autonomous follow up measurements	Atefi et al. [ 5 ] ; Zhang et al. [ 111 ] ; Khuimphukhieo and Silva [ 42 ]
Deep learning perception	Detection, segmentation, classification, and regression	Task-specific models with limited transfer and weak causal explanation	Foundation-model perception with uncertainty and human correction	Pound et al. [ 75 ] ; Murphy et al. [ 64 ] ; Li et al. [ 49 ]
Multimodal fusion	Improved prediction by combining spectral, structural, thermal, and environmental data	Fusion often remains feature-level rather than biology-level	seed soil plant environment management representation learning	Nguyen et al. [ 65 ] ; Coppens et al. [ 16 ] ; Papoutsoglou et al. [ 69 ]
Digital twins and dashboards	Monitoring, simulation, prediction, and user visualization	Many implementations are descriptive dashboards or digital shadows	Agentic digital twin with reasoning, evidence tracking, and decision support	Purcell and Neubauer [ 77 ] ; Rahman et al. [ 79 ] ; Ojo et al. [ 66 ]
Decision support	Alerts, trends, forecasts, and rule-based recommendations	Weak explanation of causes and next-best measurements	Explainable recommendations grounded in crop physiology and trial context	Walter et al. [ 101 ] ; Cesco et al. [ 13 ] ; Arshad et al. [ 3 ]

Table 8: Existing limitations in AI-driven phenotyping and how they inform the need for a PhenoAgent-based framework. The table translates the literature review into design requirements for the next generation of phenotyping.

Figure 8: Core components of PhenoAgent as a next-generation phenotyping intelligence layer that links multimodal data capture, temporal fusion, plant digital twins, agentic reasoning, and decision-ready outputs. It integrates visual, environmental, substrate, seed, and management data to explain stress, predict growth, recommend interventions, support dashboards, rank trials, and guide technology translation.

Figure 9: Closed-loop PhenoAgent workflow from phenotyping question to crop decision and outcome evaluation.

Step	Stage	Main inputs	Core function	Expected output
1	Phenotyping context definition	Crop type, growth stage, target trait, treatment, management objective, and trial metadata	Define what should be monitored, why it matters, and how crop units will be tracked over time	Crop-unit identity, trial context, and phenotyping objective
2	Multimodal data acquisition	Fixed cameras, UAV/UAD platforms, UGV robots, IoT sensors, seed records, substrate/root-zone measurements, and management logs	Collect complementary visual, environmental, biological, and management evidence from the crop system	Raw multimodal crop evidence
3	Temporal alignment and quality control	Images, sensor logs, timestamps, crop-unit IDs, and metadata	Synchronize data streams, remove low-quality records, align observations with plant/tray/plot identity, and organize measurements along the crop timeline	Clean time-aligned evidence record
4	Trait and state extraction	RGB/depth/thermal/spectral images, IoT signals, substrate data, and management events	Estimate crop traits and state indicators such as emergence, vigor, canopy growth, stress symptoms, disease cues, root-zone condition, and yield-related proxies	Trait/state profile with confidence values
5	Plant digital twin construction	Trait profile, environmental history, root-zone status, seed information, and management records	Build a time-resolved crop-state representation that links phenotype, environment, substrate/root-zone condition, and management history	Dynamic plant/tray/plot digital twin
6	PhenoAgent reasoning	Digital twin, perception outputs, crop knowledge, retrieved evidence, forecasting models, uncertainty estimates, and expert feedback	Interpret the crop state, identify likely stress drivers, compare alternative explanations, and determine whether additional evidence is needed	Explainable crop-state interpretation

Table 9: Algorithmic workflow of PhenoAgent from multimodal crop observation to decision support.

Dimension	Current Common Practice	PhenoAgent Direction	Expected Advantage	Key References
System goal	Estimate traits, indices, or labels from images and sensors	Explain crop state and recommend evidence-aware next steps	Moves from measurement to actionable intelligence	Tardieu et al. [ 89 ] ; Murphy et al. [ 64 ]
Data organization	Separate image folders, sensor logs, trial sheets, and dashboard values	Unified seed soil plant environment management time series	Enables causal interpretation across crop development	Coppens et al. [ 16 ] ; Papoutsoglou et al. [ 69 ]
AI model role	Task-specific detection, segmentation, classification, or regression	Modular perception plus foundation model and retrieval-assisted reasoning	Supports reusable and context-aware interpretation	Bommasani et al. [ 8 ] ; Li et al. [ 49 ]
Decision support	Threshold alerts, plots, and descriptive dashboards	Hypothesis ranking, uncertainty, intervention recommendation, and follow up sensing	Improves grower and breeder decision confidence	Walter et al. [ 101 ] ; Rahman et al. [ 79 ]
Human role	Manual labeling, scouting, interpretation, and final decision	Expert guided validation, feedback, correction, and approval of interventions	Preserves agronomic expertise while reducing routine burden	Wolfert et al. [ 105 ] ; Peladarinos et al. [ 71 ]
Translation path	Research prototype or sensor-specific system	Scalable proof of concept progressing toward high-TRL deployment	Converts data infrastructure into deployable phenotyping product	Atefi et al. [ 5 ] ; DigiHortiRobot [ 25 ]

Table 10: Conceptual distinction between current AI phenotyping systems and the suggested PhenoAgent framework. The table highlights why PhenoAgent should be treated as a next generation direction rather than another image-analysis pipeline.

Figure 10: Research gap-to-solution map for next generation automated phenotyping.

Gap	Current challenge	Future opportunity	Validation requirement	Key references
Dataset scarcity	Few datasets cover multiple crops, sensors, stages, environments, and management histories	Longitudinal seed soil plant datasets with real and synthetic data	Cross-site benchmark splits and metadata completeness checks	Papoutsoglou et al. [ 69 ] ; Liu et al. [ 52 ]
Annotation burden	Expert labels are expensive and inconsistent across traits and crops	Promptable segmentation, active learning, weak labels, expert-in-the-loop correction	Label agreement, uncertainty reporting, and audit trails	Kirillov et al. [ 43 ] ; Murphy et al. [ 64 ]
Benchmarking	Many studies report isolated accuracy on private datasets	Open protocols for detection, trait extraction, stress prediction, and decision value	Standard metrics for accuracy, robustness, calibration, and agronomic utility	David et al. [ 17 ] ; Jiang and Li [ 36 ]
Data standards	Image, sensor, environment, and management records are often disconnected	FAIR and MIAPPE-aligned multimodal phenotyping records	Reusable schemas linked to plant/tray/plot identity and timestamps	Coppens et al. [ 16 ] ; Papoutsoglou et al. [ 69 ]
Biological reasoning	Models detect patterns but often do not explain crop causes	Agentic multimodal reasoning with crop knowledge and digital twins	Expert agreement, causal plausibility, and intervention outcomes	Tardieu et al. [ 89 ] ; Li et al. [ 49 ]

Table 11: Key research gaps in automated phenotyping and corresponding future opportunities for PhenoAgent-style systems.

Barrier	Why it matters	Design response	TRL validation metric	Related references
Sensor robustness	Dust, humidity, lighting, drift, and occlusion reduce data quality	Calibration routines, QA flags, redundant sensing, automated health checks	Uptime, missing-data rate, calibration error	Xie and Li [ 108 ] ; Nguyen et al. [ 65 ]
Compute constraints	Real-time alerts and robotics need low latency, while training requires scale	Edge inference for alerts; cloud for training, dashboard, and historical analysis	Latency, throughput, energy use, cloud cost	Kalyani et al. [ 39 ] ; Rahman et al. [ 79 ]
Model maintenance	Crop cycles, sensors, and facilities change over time	Continuous validation, drift monitoring, versioned models, human correction	Degradation rate and retraining interval	Murphy et al. [ 64 ] ; Li et al. [ 49 ]
Data governance	Trial and yield records may be commercially sensitive	Access control, local/cloud policy, anonymization, audit logs, ownership agreement	Compliance, user acceptance, exportability	Wolfert et al. [ 105 ] ; Walter et al. [ 101 ]
Workflow integration	Systems fail if they interrupt grower or breeder routines	Dashboard co-design, role-specific views, mobile alerts, simple feedback tools	Usability, adoption, decision response time	Peladarinos et al. [ 71 ] ; Cesco et al. [ 13 ]
Commercial readiness	Research prototypes often lack support, reliability, and business model	TRL staging, pilot validation, service model, training, maintenance plan	TRL milestone completion and decision-value evidence	DigiHortiRobot [ 25 ] ; Zhang et al. [ 111 ]

Table 12: Deployment barriers and practical design responses for scalable automated phenotyping systems.

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