Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands.

Heat stress limits plant productivity by disrupting transpiration, altering leaf microclimate, and activating metabolic pathways that increase volatile organic compound (VOC) emissions. VOC signatures, combined with leaf-level relative humidity (RH), provide early indicators of plant stress, but conventional analytical methods are costly, bulky, and unsuitable for continuous in situ monitoring. Here, a low-cost multimodal sensing platform based on laser-induced graphene (LIG) is reported for real-time, on-leaf detection of methanol, acetic acid, and RH under ambient conditions. The platform integrates Pt-modified LIG electrodes with PtNP/ZnONR@ZIF-8 for methanol, PtNP/ZnONR@ZIF-8/Sn3O4 for acetic acid and GO:PDMAA for RH sensing. After optimization, the sensors respectively achieved sensitivities of −75.83 Ω/log(ppm), −1.63 Ω/ppm, and −4,776.01 Ω/%RH with detection limits of 0.382 ppm, 0.318 ppm, and 1.53 %RH and more than 97% signal retention over 26 days. On-leaf measurements over 2 weeks showed methanol increasing from ~0.3–13.8 ppm to ~13.5–59.0 ppm, acetic acid from ~6.6–14.8 ppm to ~23.3–60.6 ppm, and RH decreasing from ~72.2–86.3% to ~52.5–65.1% under heat stress. These coupled chemical and microclimate changes provide direct, dynamic stress readouts during plant monitoring. By moving beyond single-analyte measurements, the proposed multimodal approach enables early stress detection, data-driven crop management, and next-generation precision agriculture applications.

Accurate measurement of vapor pressure deficit (VPD) at the leaf surface is essential for assessing plant water status and stress. However, current greenhouse climate control strategies that rely on ambient sensors for air VPD measurements do not capture the localized microclimate at the leaf surface, limiting their usefulness for plant-centered stress detection. This study presents a low-cost, plant-integrated sensing platform for real-time, leaf-level VPD monitoring. A flexible temperature and humidity sensor suite was mounted directly on lettuce leaves grown under controlled hydroponic greenhouse conditions, enabling continuous measurement of the stomatal boundary layer microenvironment. Leaf-level temperature and relative humidity (RH) data were used to compute real-time VPD and assess plant responses under well-watered and water-stressed conditions. Sensor measurements showed that water-stressed plants consistently exhibited higher leaf-surface temperatures (~0.71 °C above controls) and lower leaf-surface RH (~8% below controls), reflecting reduced transpiration-driven cooling. Calculated VPD values ranged from 0.7-1.69 kPa under water stress compared to 0.5-1.35 kPa in control plants, with a consistent treatment difference of approximately 0.2-0.34 kPa. These trends in sensor data were validated by independent measurements using a CIRAS-4 photosynthesis system, which confirmed higher VPD and reduced stomatal conductance under stress. In contrast, ambient sensors failed to resolve these differences across treatments. These findings demonstrate that leaf-integrated sensors can bridge the gap between ambient measurements and the true leaf microclimate. The proposed platform offers a scalable and non-invasive approach for real-time VPD monitoring at the leaf-scale, supporting improved drought stress detection, irrigation scheduling, and precision climate control decisions in a greenhouse.