Cotton Canopy Research Articles

Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera.

Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (R2 = 0.35–0.71) and growth rates in early canopy development stages (R2 = 0.29–0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.

Droplet distribution of Unmanned Aerial Vehicle under several spray volumes and canopy heights in the cotton canopy

Abstract: The influences of the canopy height and the spray volume on the droplet distribution in the upper and bottom of the cotton canopy were discussed in this study. XAG P30 plant protection Unmanned Aerial Vehicle(UAV) was used as a spray tool and water sensitive paper was used as the droplet test card. The spray volumes by the UAV include 18.0 L/ha,22.5 L/ha, and 30.0 L/ha. The average planting heights of cotton in the test areas are 0.5 m,0.7m, and1.1m, respectively. The results show that the droplets are more well-distributed in the upper part of the canopy rather than the lower part. The spray volume has a significant effect on the droplet distribution in the canopy. The coverage, deposition, and droplet density will gradually increase with the increase of the application volume. The droplet distribution in the1.1m canopy area is poor, but it can be improved by increasing the spray volume. Keywords: unmanned aerial vehicle; droplet distribution; spray volume; canopy height DOI: 10.33440/j.ijpaa.20200304.136 Citation: Chen P C, Lan Y B, Douzals J, Ouyang F, Wang J, Xu W C. Droplet distribution of Unmanned Aerial Vehicle under several spray volumes and canopy heights in the cotton canopy. Int J Precis Agric Aviat, 2020; 3(4): 74–79.

Modeling Cotton Growth and Nitrogen Status Using Image Analysis

Core Ideas The study developed an image analysis approach for extracting the color features of a cotton canopy with a digital camera. The study verified the models using data collected from three fields of high‐yielding cotton. Image analysis techniques have been widely used to monitor crop growth and diagnose crop N status. The main objective of this study was to develop an image analysis method for monitoring cotton (Gossypium hirsutum L.) growth and N status with images taken by a digital camera. The experiment was consisted of two cotton cultivars grown under five different N application rates. Ten color indices were extracted from images of the cotton canopy taken between emergence and full bloom. Results showed that nine out of ten color indices were significantly correlated with N content, leaf area index (LAI), and dry matter accumulation (DMA). Among the color indices, the green minus red index (GMRI), excess green index (EGI), green to red index (GDRI), and canopy cover (CC) had the highest correlation with the three crop properties. Models were developed to describe the relationships between the color indices (GMRI, EGI, GDRI, and CC) and the crop properties (N content, LAI, and DMA). Relationships between the color indices and the three crop properties were exponential. The models were tested in three large and representative high‐yielding cotton fields. Results showed that GMRI, EGI, GDRI, and CC are valid indicators for monitoring cotton growth and N status. Digital cameras have a good potential for monitoring cotton growth and N status.

Performance Analysis of the Mechanical Behaviour of Seams With Various Sewing Parameters for Cotton Canopy Fabrics

This study aims to develop a tool to predict the behaviour under a load of textile seams, to enable the selection of materials for specified end uses in the parachute industry. The strength of an assembly is directly dependent on the strengths of the various joints and seams required to make the structure. This study seeks to understand the performance of seams woven into cotton fabrics. A combination of five parameters has been studied here. Two properties, the seam strength and seam efficiency (% ratio of seam strength to fabric strength), are used as performance estimators. Both were found to vary significantly not only with the primary parameters but also with the interactions of primary parameters as well. That is, they change with each of the primary parameters but vary in a different manner when other parameters also change. Multiple regression has been used to construct preliminary predictor equations.

Determination of biomass production of cotton using satellite images and spectral indexes

The present study aimed to evaluate the mutual dependence between the spectral behavior in the cotton canopy, the leaf area index and biomass production. The study took place, in field conditions, under irrigation, in the Busato I Farm in the city of São Desidério-BA. The 10 samples for biomass determination were taken at intervals of approximately 15 days, collecting five consecutive plants contained in 0,45m2. The designated goods MOD09GQ and MOD13Q1 were used, where you got the vegetation indices NDVI and SAVI. To calculate the Leaf Area Index three methods were tested. The accumulation of biomass was estimated by two models and these were evaluated by correlation coefficients (r), accuracy (d) and also the index (c). The maximum values for NDVI, SAVI, and total dry matter production were 0.903, 0.717 and 1200g.m-2, respectively, at 90 days after emergence. The maximum leaf area index was found at 60 days after emergence. The highest growth rate of the culture was found at 80 days after emergence and 55% of the final dry matter were flower buds. The VIs were shown to be effective for obtaining the LAI, as well as the models that bring the radiation photosynthetically active if showed consistent, because they achieved reliability values considered excellent, and correlation coefficients and higher than 90% accuracy.

USING PASSIVE AND ACTIVE MULTISPECTRAL SENSORS ON THE CORRELATION WITH THE PHENOLOGICAL INDICES OF COTTON

ABSTRACT: The investment in precision agriculture technologies has been growing as well the investment in vegetation index sensors with different types of data collection. This study aimed to evaluate the NDVI potential use obtained from passive and active multispectral sensors as phenological parameters indicator of cotton growth. The experiment was conducted in cultivated cotton fields during the harvest seasons of 2013/14 and 2014/15. The phenological variables analyzed in the field, were cotton plant height and number of branches per sampled cotton plant. The active sensors Crop Circle and Green Seeker were mounted on a manual support. The passive sensor Tetracam was installed on a support positioning the sensor at 4 m height from the ground. NDVI readings are positively correlated with plant height and number of branches per cotton plant. Active sensors present exponential correlations between NDVI and phenological readings. Passive sensor, when installed in a support above the cotton canopy level, presents linear correlation between NDVI and phenological readings. NDVI values obtained by the passive sensor are higher than the active sensors. Filtering the raw NDVI values obtained by passive sensor decreases the adjustment of the regression analyses with cotton height and number of branches per plant.

Cotton Canopy NDVI: Reducing the Ground Exposure Effect

<abstract> <b>Abstract.</b> Cotton producers aim for optimal nitrogen (N) fertilizer application rates that are correctly timed. Supplemental N is only used at the growth stage when and where it can be most effective. One management strategy monitors N levels using leaf spectral reflectance throughout the growing season. However, when the canopy is sparse or absent, a mobile implement-mounted spectral sensor is subject to influence from the exposed soil and stubble background. The results are misleading readings. This exploratory research project evaluated two vegetation index (VI) algorithms. The optimal soil-adjusted vegetation index (OSAVI) and normalized difference vegetation index (NDVI) values were selectively trimmed for improving a commercial NDVI sensing system‘s ability to discriminate between plant biomass and the ground surface. Varying plant populations, especially in early growth, can lead to erroneous real-time readings during mobile applications when passing over exposed ground surfaces. The goal of this study was to improve the real-time monitoring of cotton N status, both spatially and at the field scale. A large-scale field experiment statistically established a distribution of small contiguous plots in rows. A plot unit was a combination of one of three seeding rates, one of three cotton varieties, and one of four N application rates. Plant height, leaf N, and VI values were collected and analyzed. Both VI algorithms were found to reduce the varying plant population effect on NDVI. Plant population affected raw NDVI values throughout the season, which confirmed the effect of soil background exposure on NDVI values. Research findings suggest that after applying the two algorithms and comparing results with the non-filtered data analysis, both algorithms detected the differences due to variety, seeding rate, and N rate treatments. However, the N effect was detected earliest in the season by NDVI.

Circadian rhythms regulate the environmental responses of net CO2 exchange in bean and cotton canopies

Studies on the dependence of the rates of ecosystem gas exchange on environmental parameters often rely on the up-scaling of leaf-level response curves (‘bottom-up’ approach), and/or the down-scaling of ecosystem fluxes (‘top-down’ approach), where one takes advantage of the natural diurnal covariation between the parameter of interest and photosynthesis rates. Partly independent from environmental variation, molecular circadian clocks drive ∼24h oscillations in leaf-level photosynthesis, stomatal conductance and other physiological processes in plants under controlled laboratory conditions. If present and of sufficient magnitude at ecosystem scales, circadian regulation could lead to different results when using the bottom-up approach (where circadian regulation exerts a negligible influence over fluxes because the environment is modified rapidly) relative to the top-down approach (where circadian regulation could affect fluxes as it requires the passage of a few hours). Here we dissected the drivers of diurnal net CO2 exchange in canopies of an annual herb (bean) and of a perennial shrub (cotton) through a set of experimental manipulations to test for the importance of circadian regulation of net canopy CO2 exchange, relative to that of temperature and vapor pressure deficit, and to understand whether circadian regulation could affect the derivation of environmental flux dependencies. Contrary to conventional wisdom, we observed how circadian regulation exerted controls over net CO2 exchange that were of similar magnitude to the controls exerted by direct physiological responses to temperature and vapor pressure deficit. Diurnal patterns of net CO2 exchange could only be explained by considering effects of environmental responses combined with circadian effects. Consequently, we observed significantly different results when inferring the dependence of photosynthesis over temperature and vapor pressure deficit when using the top-down and the bottom up approaches.

Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton

Palmer amaranth (Amaranthus palmeri S. Wats.) invasion negatively impacts cotton (Gossypium hirsutum L.) production systems throughout the United States. The objective of this study was to evaluate canopy hyperspectral narrowband data as input into the random forest machine learning algorithm to distinguish Palmer amaranth from cotton. The study focused on differentiating the Palmer amaranth from cotton near-isogenic lines with bronze, green, and yellow leaves. A spectroradiometer was used to acquire hyperspectral reflectance measurements of Palmer amaranth and cotton canopies for two separate dates, December 12, 2016, and May 14, 2017. Data were collected from plants that were grown in a greenhouse. The spectral data were aggregated to twenty-four hyperspectral narrowbands proposed for study of vegetation and agriculture crops. Those bands were tested by the conditional inference version of random forest (cforest) to differentiate the Palmer amaranth from cotton. Classifications were binary: Palmer amaranth and cotton bronze, Palmer amaranth and cotton green, and Palmer amaranth and cotton yellow. Classification accuracies were verified with overall, user’s, and producer’s accuracy. For the two dates combined, overall accuracy ranged from 77.8% to 88.9%. The highest overall accuracies were observed for the Palmer amaranth versus the cotton yellow classification (88.9%, December 12, 2016; 83.3%, May 14, 2017). Producer’s and user’s accuracies range was 66.7% to 94.4%. Errors were predominately attributed to cotton being misclassified as Palmer amaranth. The overall results indicated that cforest has moderate to strong potential for differentiating Palmer amaranth from cotton when it used hyperspectral narrowbands known to be useful for vegetation and agricultural surveys as input variables. This research further supports using hyperspectral narrowband data and cforest as decision support tools in cotton production systems.

Development of Cotton Canopy Structure Characteristics of Cotton Varieties Grown in Different Decades in Northern Xinjiang

Development of Cotton Canopy Structure Characteristics of Cotton Varieties Grown in Different Decades in Northern Xinjiang

Predicting Heat Stress in Cotton Using Probabilistic Canopy Temperature Forecasts

Heat stress can reduce crop yield or even cause total crop failure. The ability to predict heat stress in advance would allow growers time to implement protective measures, helping to avoid such losses. This study presents a strategy for producing probabilistic heat stress forecasts for well‐watered cotton (Gossypium hirsutum L.) in the Camilla, GA, region. Multiple linear regression was used to develop a cotton canopy temperature model based on predicted air temperature, humidity, solar radiation, and wind speed. The European Centre for Medium‐Range Weather Forecasts Ensemble Prediction System was used to predict the meteorological variables used in the canopy temperature model, which produced 10‐d probabilistic canopy temperature forecasts for each day of observations during 2014. A statistical mean bias correction was applied to improve on the raw model forecasts. The forecasts were found to be skillful, with relative operating characteristic areas greater than 0.5. The bias‐corrected forecasts were found to increase skill. A heat stress warning system was then created using the forecasts. Additionally, an economic analysis was performed as an example of how probabilistic forecasts can be used to aid producers with financial decisions pertaining to weather‐related risks. Creating heat stress advisories for crops provide growers time to protect assets. Coupling crop and atmospheric models translates weather data into usable information for growers. Probabilistic canopy temperature forecasts can be used to evaluate on‐farm weather‐related risks.

Optimization and test for spraying parameters of cotton defoliant sprayer

Boom sprayer is widely used in large farm crops because of its high working efficiency and favorable spraying effect. But there are still some problems in cotton defoliant spraying in Xinjiang, China. Cotton is planted in a high density in Xinjiang, the row space is 10+66 cm, leaves in two adjacent rows are seriously overlapped, the lower leavers are poorly sprayed, so the defoliation effect is poor, and the cotton quality is degraded. To solve this problem and improve the defoliant droplets coverage on the cotton canopy, the original boom spraying was modified, and the spraying pardameters was optimized by the central combination test and design concept of Box-Behnken based on a single-factor test. A quadratic polynomial model of droplets coverage was created by using working parameters including horizontal spraying boom height, hang boom height and nozzles angle as the influential factors and the mean droplets coverage on cotton canopy as the target function, and the effectiveness of mode and interaction of factors were analyzed. The model was optimized and analyzed using the regression analysis method and response surface analysis method of software Design-Expert 7.0.0, and the optimal combination of spraying parameters was obtained. The results showed that the droplets coverage on cotton canopy were influenced by boom height, sprayer height and angled nozzles sequentially from large to small, and the optimal combination of spraying parameters was under horizontal spraying boom height of 134 cm, hang spraying boom height of 27.5 cm and nozzles angle of 21°. The mean droplets coverage of experimental value and predicted value on cotton canopy were 19.6% and 20.43% respectively in such conditions, and the relative error to the estimated value on the model was –4.25%. The research result can provide a reference for further optimizing the spraying parameters of cotton defoliant sprayer. Keywords: pesticide sprayer, cotton, spraying parameter, optimization, mathematical model, response surface DOI: 10.3965/j.ijabe.20160904.2125 Citation: Qin W C, Xue X Y, Cui L F, Zhou Q Q, Xu Z F, Chang F L. Optimization and test for spraying parameters of cotton defoliant sprayer. Int J Agric & Biol Eng, 2016; 9(4): 63－72.

Monitoring of Aphis gossypii Using Greenseeker and SPAD Meter

Cotton aphid (Aphis gossypii) is considered as one of the most important agriculture pest for the cotton production. However, it is generally labor-intensive and time-consuming to obtain some information of Cotton aphid with conventional methods through direct measurement by sampling in the field. This study explores the potential of using a new method to obtain information of the Cotton aphid rapidly. In our study, the cotton canopy spectral indices (NDVI, VI_2, REDrefc, NIRrefc) and chlorophyll concentration, obtained from hand-held high spectrometer GreenSeeker and chlorophyll meter SPAD-502 and Cotton aphid amount derived from the artificial field-based survey were used to uncover the relationship between Cotton aphid amount and canopy spectral index and SPAD value of the cotton in city of Shihezi, China. The results showed that NDVI and NIRrefc were negatively related to Cotton aphid amount. VI_2 content had a significant and positive relationship with its amount. The non-linear three cubic models with alate Aphid amount as independent variables have been established between VI_2 value and alatae Aphid amount, which could explain 92.37 % of VI_2 value variance. SPAD values were also significantly and negatively correlated to the Aphid amount. The non-linear logarithm model with wingless Aphid amount as independent variables was the best for uncovering the relationship between SPAD value and wingless Aphid amount, which could explain 85.48 % of SPAD value variance. The results demonstrate the establishment of the function model provides a theoretical basis and techniques for indirect and rapid monitoring and management of Cotton aphid.

Cotton responds to different plant population densities by adjusting specific leaf area to optimize canopy photosynthetic use efficiency of light and nitrogen

Appropriate plant population density (PPD) is an important crop management practice for optimizing canopy light distribution and increasing canopy photosynthetic capacity in field-grown cotton (Gossypium hirsutum L.). A 2-year field experiment was conducted to determine how the PPD (7.5, 19.5 or 31.5 plantsm−2) of cotton affects canopy photosynthetic capacity and photosynthetic use efficiency of light and N. The results indicated that PPD significantly affected both leaf morphology and canopy photosynthetic characteristics. As PPD changed, cotton maximized canopy apparent photosynthetic light use efficiency (CAPLUE) and photosynthetic capacity by adjusting specific leaf area (SLA), which in turn affected leaf N distribution in the canopy. The SLA of all three canopy layers increased as PPD increased. In the upper canopy, canopy light interception and canopy apparent photosynthetic N use efficiency (CAPNUE) rose as SLA increased, but CAPLUE declined. As PPD increased, SLA in the mid- and lower-canopy layers increased significantly. This caused canopy apparent photosynthesis rate per leaf area (CAPLeaf) and CAPNUE to decline. Medium-PPD had the highest canopy apparent photosynthesis rate (CAP) and CAPLUE in the mid- and lower-canopy layers. As a result, medium-PPD had the highest whole-canopy photosynthetic capacity and CAPLUE in this study. Overall, the results indicated that optimal spatial distribution of both light and specific leaf area is key for efficient utilization of light and N in cotton canopies.

Physiological characteristics of high yield under cluster planting: photosynthesis and canopy microclimate of cotton

Cotton produces more biomass and economic yield when cluster planting pattern (three plants per hole) than in a traditional planting pattern (one plant per hole), even at similar plant densities, indicating that individual plant growth is promoted by cluster planting. The causal factors for this improved growth induced by cluster planting pattern, the light interception, canopy microclimate and photosynthetic rate of cotton were investigated in an arid region of China. The results indicated that the leaf area index and light interception were higher in cluster planting, and significantly different from those in traditional planting during the middle and late growth stages. Cotton canopy humidity at different growth stages was increased but canopy temperatures were reduced by cluster planting. In the later growth stage of cluster planting, the leaf chlorophyll content was higher and the leaf net photosynthetic rate and canopy photosynthetic rate were significantly increased in comparing with traditional planting pattern. We concluded that differences in canopy light interception and photosynthetic rate were the primary factors responsible for increased biomass production and economic yield in cluster planting compared with the traditional planting of cotton.

A new approach to estimate canopy evaporation and canopy interception capacity from evapotranspiration and sap flow measurements during and following wetting

Canopy interception and its evaporation into the atmosphere during irrigation or a rainfall event are important in irrigation scheduling, but are challenging to estimate using conventional methods. This study introduces a new approach to estimate the canopy interception from measurements of actual total evapotranspiration (ET) using eddy covariance and estimation of the transpiration from measurements of sap flow. The measurements were conducted over a small-scale sprinkler-irrigated cotton field before, during and after sprinkler irrigation. Evaporation and sap flow dynamics during irrigation show that the total ET during irrigation increased significantly because of the evaporation of free intercepted water while transpiration was suppressed almost completely. The difference between actual ET and transpiration (sap flow) during and immediately following irrigation (post irrigation) represents the total canopy evaporation while the canopy interception capacity was calculated as the difference between actual ET and transpiration (sap flow) during drying (post irrigation) following cessation of the irrigation. The canopy evaporation of cotton canopy was calculated as 0.8 mm, and the interception capacity was estimated to be 0.31mm of water. The measurement uncertainty in both the non-dimensional ET and non-dimensional sap flow was shown to be very low.

Optimizing canopy photosynthetic rate through PAR modeling in cotton (Gossypium spp.) crops

A study of the distribution of photosynthetically active radiation (PAR) within cotton (Gossypium spp.) canopies of varying architecture is presented. Based on this study, a computer model is formulated to estimate PAR distribution and photosynthetic rates of cotton canopies. The model is developed from photon flux densities (PFD) measured at several stages of development in row-planted cotton canopies. The photon transport equation is implemented into the model along with an algorithm to calculate canopy photosynthetic rates. The resulting model is then used to generate different scenarios of plant architecture to identify the canopy architecture with optimal canopy photosynthetic rates.The results demonstrate that the differences in light distribution within the different cotton canopies were successfully captured. The model reproduces PAR distributions successfully for the cotton cultivars included in the study (R2>0.9 for most cultivars).Photosynthetic rate estimations showed that the structure of the DP-50 canopy is the most efficient canopy architecture by allowing PAR penetration throughout its canopy. This cultivar also uses the available sunlight more efficiently by enhancing PAR interception. The optimization process suggests that a cotton plant could increase its photosynthetic rate by 10% by distributing leaves within the canopy similarly to that of DP-50, and with leaves shaped like small-to-medium PS-6 leaves.

Spatial distribution of light interception by different plant population densities and its relationship with yield

Light interception (LI) by the canopy is an important environmental factor that determines dry matter production and crop development. Using a geostatistical sampling-based method, we studied the spatial distribution of LI at different points, in different layers and profiles and in the entire cotton canopy. The results showed that the LI decreased with increasing height. During early development, the horizontal changes in LI were large for a given vertical point. However, the horizontal changes in LI were smaller during later development. Profile maps of the LI for the six plant densities showed that different LIs occurred during the early growth stage. In addition, the linear regressions between the total accumulated LIs of the different layers and the cottonseed yield revealed that the LI in the bottom layer and in the middle of two cotton rows significantly contributed to the cottonseed yield. Thus, a higher economic yield could be obtained by improving the micro-environment in these two layers.

The relationship between cotton canopy temperature and yield, fibre quality and water-use efficiency

Crop canopy temperature (Tc) is associated with transpiration and Tc has been used in crop water stress detection. This study investigates the effect of surface drip and furrow irrigation regimes on cotton Tc. It outlines the relationship between Tc and lint yield, fibre quality and both agronomic (WUEa, kgha−1mm−1 total applied water) and leaf level water-use efficiency (WUEl, μmol[CO2] mmol[H2O]−1) in a high input, high yielding (>1800kgha−1) cotton system. Canopy temperature between flowering and crop maturity was monitored. Yield reductions occurred when Tc exceeded 28°C. Reductions in fibre length outside the ideal range (>28.6mm) occurred when Tc exceeded 31°C, while desirable micronaire (3.8–4.5) was observed at Tc between 25 and 32°C. Desirable fibre quality and peak lint yield can be realised if an irrigation scheduling protocol maintains average canopy temperatures below 28°C. However, maximum WUEa was observed at a higher average Tc (30°C) than peak lint yield (28°C), which would correspond to a predicted 23% reduction in lint yield from the peak (3030kgha−1). Therefore, the trade-off between peak yield and WUEa needs to be considered in conjunction with irrigation water costs and availability when scheduling irrigations based on canopy temperature.

Water Deficit Alters Cotton Canopy Structure and Increases Photosynthesis in the Mid‐Canopy Layer

Little is known about how water deficit affects cotton (Gossypium hirsutum L.) canopy architecture and the vertical distribution of photosynthesis within the canopy. The objective of this 2‐yr field experiment was to determine the effects of reduced water supply on (i) surface area distribution within a cotton canopy, (ii) the transmission of photosynthetically active radiation (PAR) within the canopy, and (iii) the contribution of leaves and non‐leaf organs at different positions within the canopy to whole‐canopy photosynthesis. The results showed that compared with conventional irrigation, water deficit reduced leaf surface area in the upper canopy layer by 20 to 46% and increased PAR transmission into the mid‐canopy layer by 38 to 73%. Slight water deficit reduced leaf photosynthetic rates in the upper canopy layer by 24%, but increased leaf photosynthetic rates in the mid‐canopy layer by 23% and the lower canopy layer by 79%. Compared with conventional irrigation, slight water deficit had no significant effect on yield, whereas moderate water deficit significantly reduced both variables. Leaves, especially those in the upper canopy layer, were the main drivers of whole‐canopy photosynthesis. On a percentage basis, non‐leaf organs accounted for &lt;26% of whole‐canopy photosynthesis. Future research is needed to learn more about the photosynthetic potential of non‐leaf organs and their response to water deficit.