Lettuce Canopy Research Articles

Side Lighting of Red, Blue and Green Spectral Combinations Altered the Growth, Yield and Quality of Lettuce (Lactuca sativa L. cv. "Yidali") in Plant Factory.

A plant factory with artificial lighting (PFAL) usually uses top lighting for cultivation. The light from the upper part of the canopy cannot penetrate the entire lettuce canopy, however, resulting in uneven vertical spatial light in the canopy, and accelerating the senescence of both the bottom and side leaves of the plant canopy. Therefore, in this study, the performance of lettuce in hydroponics was investigated upon supplemental side lighting with different spectral LEDs in a PFAL. A set of short-term side lighting treatments, including no side lamps (CK), red (R), blue (B), red + blue (RB), and red + blue + green (RGB) LED lamps (150 μmol·m-2·s-1, respectively), was employed for an additional 2 h per day after normal top lighting for 6 days before harvest. The results showed that the lettuce canopy was relatively loose and had a large crown size under side lighting compared with CK. Side lighting, irrespective of spectral qualities, significantly increased the fresh weight, and the R, B, RB, and RGB treatments increased the shoot fresh weight of lettuce plants by 34%, 19%, 31%, and 34%, and increased the fresh weight of leaf layer 2 by 50%, 17%, 44%, and 48%, respectively. The side lighting of different spectral qualities had a significant impact on the nutritional quality of the first row of lettuce at the edge of the top lighting illuminated area. Treatment B significantly promoted the chlorophyll content of leaf layer 3; the soluble sugar contents from leaf layer 1, 2, and 3; the starch contents in leaf layers 2 and 3; and the content of phenolics in the leaf layers 3; and significantly reduced the nitrate content in leaf layers 2 and 3. RGB significantly increased soluble sugar content by 91%, and the starch content in leaf layer 1, as well as the leaf chlorophyll and flavonoid content of leaf layer 3, while R had opposite effect completely. RB significantly increased the leaf chlorophyll content of leaf layer 3 and the nitrate content in leaf layer 1, but the overall effect was lower than that of RGB. In summary, side lighting of any type could effectively improve lettuce yield, solve the problem of inconsistent lettuce plant size caused by the edge effect of top lighting, and affect the nutritional quality of lettuce. B and RGB performed best. There was spatial response diversity of lettuce plants to side lighting spectral qualities.

Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data

The fresh weight of vegetables is an important index for the accurate evaluation of growth processes, which are affected by factors such as temperature and radiation fluctuation, especially in a passive solar greenhouse. Predicting dynamic growth indexed by fresh weight in a solar greenhouse remains a challenge. A novel method for predicting the dynamic growth of leafy vegetables based on the in situ sensing of phenotypic and environmental data of batches is proposed herein, enabling prediction of the dynamic fresh weight of substrate-cultivated lettuce grown in a solar greenhouse under normal water and fertilizer conditions. Firstly, multibatch lettuce cultivation experiments were carried out and batch datasets constructed by collecting growth environmental data and lettuce canopy images in real time. Secondly, the cumulative environmental factors and instantaneous fresh weights of the lettuce batches were calculated. The optimum response time in days was then explored through the most significant correlations between cumulative environmental factors and fresh weight growth. Finally, a dynamic fresh weight prediction model was established using a naive Bayesian network, based on cumulative environmental factors, instantaneous fresh weight, and the fresh weight increments of batches. The results showed that the computing time setpoint of cumulative environmental factors and instantaneous fresh weight of lettuce was 8:00 AM and the optimum response time was 12 days, and the average R2 values among samples from three batches reached 95.95%. The mean relative error (MRE) of fresh weight prediction 4 days into the future based on data from the current batch was not more than 9.57%. Upon introducing another batch of data, the prediction 7 days into the future dropped below 8.53% MRE; upon introducing another two batches, the prediction 9 days into the future dropped below 9.68% MRE. The accuracy was improved by the introduction of additional data batches, proving the model’s feasibility. The proposed dynamic fresh weight growth prediction model can support the automatic management of substrate-cultivated leafy vegetables in a solar greenhouse.

Detection of Water Content in Lettuce Canopies Based on Hyperspectral Imaging Technology under Outdoor Conditions

To solve the problem of non-destructive crop water content of detection under outdoor conditions, we propose a method to predict lettuce canopy water content by collecting outdoor hyperspectral images of potted lettuce plants and combining spectral analysis techniques and model training methods. Firstly, background noise was removed by correlation segmentation, proposed in this paper, whereby light intensity correction is performed on the segmented lettuce canopy images. We then chose the first derivative combined with mean centering (MC) to preprocess the raw spectral data. Hereafter, feature bands were screened by a combination of Monte Carlo uninformative variable elimination (MCUVE) and competitive adaptive reweighting sampling (CARS) to eliminate redundant information. Finally, a lettuce canopy moisture prediction model was constructed by combining partial least squares (PLS). The correlation coefficient between model predicted and measured values was used as the main model performance evaluation index, and the modeling set correlation coefficient Rc was 82.71%, while the prediction set correlation coefficient RP was 84.67%. The water content of each lettuce canopy pixel was calculated by the constructed model, and the visualized lettuce water distribution map was generated by pseudo-color image processing, which finally revealed a visualization of the water content of the lettuce canopy leaves under outdoor conditions. This study extends the hyperspectral image prediction possibilities of lettuce canopy water content under outdoor conditions.

Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning.

Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination (R2) of 0.938 over the entire lettuce growth period. The experiment results demonstrated that the multi-modal fusion method could improve the fresh weight estimation performance by leveraging the advantages of empirical geometric traits and deep neural features simultaneously.

Soil-biodegradable mulch is an alternative to non-biodegradable plastic mulches in a strawberry-lettuce double-cropping system

Double-cropping strawberry (Fragaria × ananassa Duch.) and lettuce (Lactuca sativa L.) could be a sustainable alternative practice for diversified specialty crop growers. Plastic mulch is beneficial for strawberry and lettuce production with soil-biodegradable mulch (BDM) providing opportunities to reduce plastic waste generation and costs of mulch disposal. The objective of this study was to compare non-biodegradable plastic mulches and BDM to bare ground cultivation in a strawberry–lettuce double-cropping system in a Mediterranean climate. A split-plot randomized complete block design experiment with mulch treatment [non-biodegradable polyethylene (PE) film, “weedmat” (woven, PE-based), BDM, and bare ground control] as the main factor and double-cropping rotation scheme (rotation 1 or 2) as the split-plot factor was conducted between 2020 and 2021. Strawberry yield was overall greatest when mulched with BDM, PE, and weedmat compared to the bare ground control. Total soluble solids (TSS), pH, and total titratable acidity (TA) of strawberry differed due to harvest date, but only pH differed due to mulch treatment. For lettuce canopy cover, rotation 2 had greater canopy cover (86%) compared with rotation 1 (66%) 30 days after transplanting. Average head weight (0.4 kg) and head length and diameter (both 20 cm) did not differ due to mulch or rotation. Average soil temperatures under PE and BDM were ~1.5°C higher than under weedmat and 2.5°C higher than the bare ground control. Soil under weedmat had the highest moisture content, whereas the PE, BDM, and bare ground treatments had similar levels. PE, BDM, and weedmat provided equivalent weed suppression, whereas the bare ground control had the most weeds. BDM deterioration estimated as percent soil exposure (PSE) was greatest (59% PSE) by the end of the experiment, whereas PE had 18% PSE and weedmat had no deterioration. Results show that despite high levels of deterioration, BDM performs similarly to PE in terms of soil temperature and moisture modification, weed suppression, and promotion of crop growth and yield in a double-cropped system. Double-cropping strawberry and lettuce is a promising alternative for growers to diversify their crop type and with potential economic benefits by increasing mulch lifespan.

Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

Describing Lettuce Growth Using Morphological Features Combined with Nonlinear Models

The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R2 (RTPCA2=0.9097, RTPCP2=0.8536) and lower MAPE (MAPETPCA=0.0284, MAPETPCP=0.0794) values, whereas the Logistic model produced a better fit for changes in PH (RPH2=0.8991, MAPEPH=0.0344). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.

Greenhouse-based vegetable high-throughput phenotyping platform and trait evaluation for large-scale lettuces

Large-scale screening and assessment of lettuce resources are valuable to help discover significant traits and assist in genetic breeding. In this research, a greenhouse-based vegetable high-throughput phenotyping platform (VHPP) was established to evaluate the multidimensional characteristics of various lettuce varieties. The platform contains an imaging unit with four degrees of freedom (DOFs) to cruise on the crop overhead for image acquisition. The platform also has an automated global semantic phenotyping pipeline (GSPP) for locating pots in sequential images and matching each plant at different growth points. Multidimensional image-based traits were automatically extracted and classified into six categories, including geometry, structure, texture, color, color moment and color indices. We calculated and evaluated 63 static traits (ST) and 189 dynamic traits (DT) by principal component (PC), correlation and heritability analysis, and new PCs provided valuable perspective in describing the lettuce canopy. The results demonstrated the ability of a phenotyping system and pipeline to rapidly investigate and evaluate the growth status of thousands of vegetables. Besides, we identified lots of valuable traits that could be of positive significance in revealing the genetic basis of complex features and exploring the excellent attributes for use in the large-scale lettuce screening and assessment.

The Effects of Black and White Plastic Mulch on Soil Temperature and Yield of Crisphead Lettuce in Southern New England

Annually, Americans consume an average of 24.5 lb of lettuce (Lactuca sativa) per capita, more than half of which is head lettuce. This study examined the impacts of using black and white-on-black polyethylene mulches on three crisphead lettuce cultivars for spring production in the open field, with data collected on the soil temperature and lettuce yields. Black polyethylene, white-on-black polyethylene, and bare ground were compared for effects on soil temperature, lettuce yields, and lettuce head height and diameter. Mean soil temperatures at a 5 cm depth were 18.9 °C under black polyethylene, 17.7 °C under white-on-black polyethylene, and 17.1 °C in bare ground plots. Changes in the lettuce canopy size presented a similar trend over the growing season in all treatments. Both mulch type and cultivar significantly (P < 0.01) affected the canopy growth in head lettuce. Lettuce on black polyethylene mulch grew significantly (P < 0.01) faster than lettuce on white-on-black polyethylene or bare ground. However, the black and white-on-black mulches produced similar yields, averaging 5.76 and 5.71 kg·mˉ2, respectively. Meanwhile, bare ground plot yields were significantly (P < 0.01) lower at 4.57 kg·mˉ2. Cultivar rank order was consistent across treatments, and Crispino and Garmsir at 5.82 and 5.47 kg·mˉ2 fresh weight had significantly higher yields than Nevada at 4.75 kg·mˉ2 (P < 0.01).

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

Leaf canopy area is a fundamental crop growth characteristic that encompasses the spatial area covered by plants. However, non-destructive and automatic computation of lettuce canopy area is still open research. This study presents a vision-based system with color space thresholding and machine learning models in measuring the photosynthetic productivity of aquaponic lettuce based on canopy area derived from the numerical image textural features of Haralick and gray level co-occurrence matrix (GLCM). Lettuce images on different growth stages with varying photosynthetic pigment intensities and geometrical structures are extracted with contrast, correlation, energy, homogeneity, entropy, variance, and information measure of correlations 1 and 2 features. For multi-band color space thresholding, CIELab bested RGB, HSV, and YCbCr colour spaces in segmenting the lettuce plant with sensitivity and specificity measures of 94.77% and 97.16% respectively. For measuring the lettuce canopy area, RMSE was recorded as 50.23% for fitness function neural network (FFNN), 20.46% for radial basis function neural network (RBFNN), 15.11% for exact radial basic function neural network (RBEFNN) and 13.54% for generalized regression neural network (GRNN). Comparative analysis revealed that the two-hidden layer GRNN model with 0.09 spread value and 240 hidden neurons bested other machine learning models in terms of RMSE without overfitting.

A CFD analysis on improving lettuce canopy airflow distribution in a plant factory considering the crop resistance and LEDs heat dissipation

In a plant factory, improper design of the indoor ventilation system may cause tip burn on lettuce plants due to the existence of stagnant air in the leaf boundary layer and low transpiration rates. In this study, three types of air ducts with different pore numbers and diameters were designed to generate a constant horizontal airflow on the surface of plant canopy. A three-dimensional computational fluid dynamics (CFD) model was established to analyse the airflow pattern on the crop canopy under different ventilation modes in a single cultivation bed. For this simulation model, the lettuce leaves were treated as a porous medium, and the drag coefficient (CD) was assumed to be 0.02, which resulted in a permeability (Kp) and nonlinear momentum loss coefficient (CF) of 0.02 and 0.134, respectively. The LED lamps were set as the energy source, and the heat released was calculated to be 297.5 kW m−3. All the data above were validated. Three Designs with different pore numbers and diameters were simulated to predict the distribution of airflow. The design with 12 pores and 15 mm diameter was predicted to give the highest percentage of mean air velocity between 0.28 m s−1 and 1.04 m s−1 on the lettuce canopy surface, at 70.3%. This duct design is recommended as the best of the three Designs in this study.

Effects of root zone cooling on butterhead lettuce grown in tropical conditions in a coir-perlite mixture

Growing temperate vegetable crops such as lettuce in the humid tropical climate of lowland Malaysia commonly results in heat-induced crop damage, delayed heading, and precocious bolting. Manipulating the root zone to a cooler temperature may alleviate plant damage caused by high ambient temperatures to a large extent. In this study, the effects of root zone cooling (RZC) on the growth and development of butterhead lettuce were studied in a containerized planting system containing a 3:1 coir-perlite mixture. Polyvinyl chloride (PVC) pipes were used to circulate cold water and cool the root zone of the experimental plants, while their shoots were exposed to the hot ambient temperature. The temperature of the root zone was measured at different depths (50 mm, 100 mm, 150 mm, and 200 mm from the surface of the media). The butterhead lettuce canopy diameter and number of leaves were significantly higher in the RZC treatment at the end of the growth stage compared to the control plants, and the yield, as measured by the shoot and root dry weight, as well as the root/total biomass ratio were also improved. The cooling pipes placed 100 mm below the surface provided a better growth condition for butterhead lettuce; at 100 mm, the RZC media had a mean daily temperature of 19°C and a minimum temperature of 14.4°C at 18:00. This study demonstrated the importance of optimizing root temperature for butterhead lettuce in an ambient hot tropical climate.

Improvement of Canopy Light Distribution, Photosynthesis, and Growth of Lettuce (Lactuca Sativa L.) in Plant Factory Conditions by Using Filters to Diffuse Light from LEDs

Plant factories with artificial lights require a large amount of electrical energy for lighting; therefore, enhancement of light use efficiency will decrease the cost of plant production. The objective of this study was to enhance the light use efficiency by using filters to diffuse the light from LED sources in plant factory conditions. The two treatments used diffuse glasses with haze factors of 40% and 80%, and a control without the filter. For each treatment, canopy light distribution was evaluated by a 3-D ray tracing method and canopy photosynthesis was measured with a sealed acrylic chamber. Sixteen lettuces for each treatment were cultivated hydroponically in a plant factory for 28 days after transplanting and their growth was compared. Simulation results showed that the light absorption was concentrated on the upper part of the lettuce canopy in treatments and control. The control showed particularly poor canopy light distribution with hotspots of light intensity; thus the light use efficiency decreased compared to the treatments. Total light absorption was the highest in the control; however, the amount of effective light absorption was higher in treatments than the control, and was highest in treatment using filters with a haze factor of 80%. Canopy photosynthesis and plant growth were significantly higher in all the treatments. In conclusion, application of the diffuse glass filters enhanced the canopy light distribution, photosynthesis, and growth of the plants under LED lighting, resulting in enhanced the light use efficiency in plant factory conditions.

Nondestructive measurement of total nitrogen in lettuce by integrating spectroscopy and computer vision

This study was conducted to develop and assess a method of estimating the nitrogen (N) content of lettuce (Lactuca sativa) canopy using a combination of spectroscopy and computer vision for nondestructive N detection. In the experiment, 90 lettuce samples with five N treatments were collected for data acquisition by two different techniques. On the spectroscopy side, canopy spectral reflectance was measured in the wavelength range of 350 to 2500nm at 1-nm increments. Four spectral intervals (376 variables) were selected by synergy interval partial least squares and were further reduced to 73 wavelength variables, chosen using a genetic algorithm applied to first-order derivatives of the canopy reflectance. On the computer vision side, 11 plant features were extracted from images, including top projected canopy area as a morphological feature; red, green, blue, hue, saturation, and intensity values as color features; and contrast, entropy, energy, and homogeneity as textural features. Next, principal component analysis was implemented on the spectral variables and on the image features, and extreme learning machine modeling was used to fuse the two kinds of data and construct a model. For the optimum model achieved, the root-mean-square error of prediction=0.3231% and the correlation coefficient of prediction=0.8864. This work demonstrates that integrating spectroscopy and computer vision with suitable efficient algorithms has high potential for use in the nondestructive measurement of N content in lettuce, considerably improving accuracy over that using a single sensor modality.

Digital Image Analysis to Supplement Direct Measures of Lettuce Biomass

Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. This work sought to establish the limits to which image acquisition and analysis may replace standard, destructive measures of fresh lettuce biomass. Outdoor, high tunnel, and greenhouse plantings of three cultivars of red and green leaf lettuce (Lactuca sativa) were direct-seeded in raised beds and plastic trays in spring, summer, and fall seasons in 2009–10 in Wooster, OH. Overhead images (624 in total) were captured at specific time points after seeding using handheld and tripod-mounted commercial digital cameras. Fresh weight and leaf area of destructive plant samples within the digital images were also collected. Images were analyzed using user-defined settings in WinCAM software (Regent Instruments, Quebec, QC, Canada). A reference grid captured within each image allowed for the calculation of crop canopy cover (percent of two-dimensional image area covered by leaves). Calculations of canopy cover require differentiating leaves and rooting medium by color. The rooting medium was dark in color, and differentiating red leaves against this background was less reliable than differentiating green leaves from background. Nevertheless, in samples collected in the greenhouse 7 to 16 days after sowing (DAS), significant correlations (r) of 0.85 to 0.96 (P < 0.05) were observed between measures of canopy cover calculated by image analysis software and leaf area obtained with a leaf area meter on harvested plant material. In outdoor and high tunnel plots 16 to 30 DAS, correlation coefficients between direct measures of plant biomass and WinCAM estimates of canopy cover were 0.71 to 0.95 (P < 0.0001). We conclude that digital image analysis may be useful in real-time, nondestructive assessments of early stage leaf lettuce canopy development, particularly when the leaf area index (LAI) is less than one and settings are dominated by green leaves.

Predicting Lettuce Canopy Photosynthesis with Statistical and Neural Network Models

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Canopy photosynthesis of CO2‐enriched lettuce (Lactuca sativa L.). Response to short‐term changes in CO2, temperature and oxides of nitrogen

SUMMARYThe canopy net photosynthesis (Pn) of lettuce (Lactuca sativa L. ev.‘Ambassador’) was analyzed under controlled conditions simulating the winter glasshouse atmosphere. Prior to measurements the plants were grown in CO2‐enriched air of 1000 μmol mol−1, at a photosynthetic photon flux density (PPFD) of 280 μmol m2 s−1 (400–700 nm) and a day/night air temperature of 16/13 °C.Short‐term changes in CO2 concentration significantly changed the initial gradient of the photosynthetic response to incident PPFD. Maximum photosynthetic efficiency of the crop increased from 0·041 mol CO2 mol photons−1 (equivalent to 8·2 μg CO2 J−1 and 9·4% on an energy basis) at 350 μmol mol−1 to 0·055 mol CO2 photons−1 (10·9 μg CO2 J−1 and 12·7% on an energy basis) at 1000 μmol mol−1. Transfer from low to high CO2 also lowered the light compensation point, but did not affect dark respiration. The large response of Pn to transient changes in CO2 indicated that the lettuce canopy did not acclimate to growth in 1000 μmol CO2 mol−1, in contrast with the effect of growth in high CO2on Pn in single mature leaves reported earlier.A reduction in air temperature from 16 to 6 °C at a concentration of 1000 μmol CO2 mol−1 halved the rate of dark respiration and reduced the light compensation point, but had no direct effect on the maximum efficiency with which the crop utilized light. Subsequently at low light (below 200 μmol m−1 s−1) Pn was greater at 6 than 16 °C. Between a PPFD of 250 and 300 μmol m−2 s−1 canopy Pn was similar at all temperatures.Addition of 2·0 μmol mol−1 nitric oxide to an atmosphere of 1000 μmol CO2 mol−1 caused a rapid and reversible reduction of canopy Pn, which was greater at the lowest temperatures. The average inhibition was 6·6 at 16 °C and 28·8% at 6 °C; this was not explained by differences in the rate of pollutant uptake, which was less in the cooler conditions.The results are discussed in relation to development of optimal growing conditions for production of glasshouse lettuce at low light and low temperature during winter in the UK.