Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought

Usefulness of digital infrared thermography video using the FLIR T560 in detecting hypothermia associated with lumbosacral radiculopathy following spinal stenosis: A CARE-compliant case report

RATIONALE

Here, we investigated the effectiveness of the FLIR T560 thermal imaging camera in detecting hypothermic changes associated with radiculopathy caused by spinal stenosis. This study aimed to determine whether the FLIR T560 could serve as a portable, efficient tool for diagnosing radiculopathy and its related sympathetic dysfunction in clinical practice.

PATIENT CONCERNS

A 77-year-old male had a 1-year history of neuropathic pain in the right distal lower leg, confirmed as L5 and S1 radiculopathy due to central stenosis at L4-5 and L5-S1, as shown by MRI and electrodiagnostic studies. The FLIR T560 recorded thermal images in an insulated room, revealing a significantly lower surface temperature (approximately 1.0°C difference) on the lateral and posterior right distal lower leg and right toes compared with the left side. By referencing the color map and scale, we estimated that the relative temperature difference between corresponding areas was approximately 1.0°C.

DIAGNOSES

L5 and S1 radiculopathy due to central stenosis at L4-5 and L5-S1.

INTERVENTIONS

A transforaminal epidural steroid injection targeting the right L5 and S1 nerve roots was conducted.

OUTCOMES

The patient's pain improved significantly.

LESSONS

The FLIR T560 camera offers a portable and convenient alternative to traditional digital infrared thermographic imaging, allowing real-time thermal imaging in a clinical setting without the need for additional equipment. Our case report suggests that the FLIR T560 is a valuable tool for detecting sympathetic dysfunction associated with radiculopathy. Further studies with larger patient populations are recommended to validate its clinical utility.

Hyperspectral Imaging Reveals Differential Carotenoid and Chlorophyll Temporal Dynamics and Spatial Patterns in Scots Pine Under Water Stress

Drought-related die-off events have been observed throughout Europe in Scots pine (Pinus sylvestris L.). Such events are exacerbated by carbon starvation that is, an imbalance of photosynthetic productivity and resource usage. Recent evidence suggests that optically measurable photosynthetic pigments such as chlorophylls and carotenoids respond to water stress (WS). However, there is a lack of measurements using imaging spectroscopy, and the mechanisms linking xanthophyll-related changes in reflectance captured by the photochemical reflectance index (PRI) and chlorophyll changes in red edge position (REP) to WS are not understood. To probe this, we conducted a greenhouse experiment where 3-year-old Pinus sylvestris saplings were subjected to water limitation and followed using hyperspectral imaging (HSI) spectroscopy, water status and photosynthetic measurements. Carotenoids (e.g., xanthophyll cycle) and chlorophylls responded to WS, which was observed using the HSI-derived indices PRI and REP respectively. The spatial-temporal response in these two pigment-reflectance groupings differed. The spatial distribution of PRI represented the light intensity around the time of the measurement, whereas REP reflected the daily averaged light intensity over the experimental course. A further difference was noted upon rewatering, where the carotenoid-related PRI partially recovered but the chlorophyll-related REP did not.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Uncovering the wide protective responses in Coffea spp. leaves to single and superimposed exposure of warming and severe water deficit

Climate changes boosted the frequency and severity of drought and heat events, with aggravated when these stresses occur simultaneously, turning crucial to unveil the plant response mechanisms to such harsh conditions. Therefore, plant responses/resilience to single and combined exposure to severe water deficit (SWD) and heat were assessed in two cultivars of the main coffee-producing species: Coffea arabica cv. Icatu and C. canephora cv. Conilon Clone 153 (CL153). Well-watered plants (WW) were exposed to SWD under an adequate temperature of 25/20°C (day/night), and thereafter submitted to a gradual increase up to 42/30°C, and a 14-d recovery period (Rec14). Greater protective response was found to single SWD than to single 37/28°C and/or 42/30°C (except for HSP70) in both cultivars, but CL153-SWD plants showed the larger variations of leaf thermal imaging crop water stress index (CWSI, 85% rise at 37/28°C) and stomatal conductance index (IG, 66% decline at 25/20°C). Both cultivars revealed great resilience to SWD and/or 37/28°C, but a tolerance limit was surpassed at 42/30°C. Under stress combination, Icatu usually displayed lower impacts on membrane permeability, and PSII function, likely associated with various responses, usually mostly driven by drought (but often kept or even strengthened under SWD and 42/30°C). These included the photoprotective zeaxanthin and lutein, antioxidant enzymes (superoxide dismutase, Cu,Zn-SOD; ascorbate peroxidase, APX), HSP70, arabinose and mannitol (involving de novo sugar synthesis), contributing to constrain lipoperoxidation. Also, only Icatu showed a strong reinforcement of glutathione reductase activity under stress combination. In general, the activities of antioxidative enzymes declined at 42/30°C (except Cu,Zn-SOD in Icatu and CAT in CL153), but HSP70 and raffinose were maintained higher in Icatu, whereas mannitol and arabinose markedly increased in CL153. Overall, a great leaf plasticity was found, especially in Icatu that revealed greater responsiveness of coordinated protection under all experimental conditions, justifying low PIChr and absence of lipoperoxidation increase at 42/30°C. Despite a clear recovery by Rec14, some aftereffects persisted especially in SWD plants (e.g., membranes), relevant in terms of repeated stress exposure and full plant recovery to stresses.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.