Crop Canopy Research Articles

Analysis of climate uniformity in indoor plant factory system with computational fluid dynamics (CFD)

Air movement greatly affects plant growth and therefore providing adequate air movement and uniform growth environment is important. Computational fluid dynamics (CFD) simulations were applied to study transport phenomena of flow fields inside a commercial, large-scale indoor plant factory, having the goal to improve climate uniformity and subsequently crop uniformity. Five designs of ventilation system were compared with different combinations of air supply vents and return air outlets with steady-state simulation. The climate uniformity was compared with relative standard deviation (RSD) in five cases for air current speed (m s −1 ), air temperature (K), and vapour pressure deficit (Pa) at just above the top surfaces of crop canopies. These included Case 1 — with supply air inlets and return air outlets installed on opposite side walls; Case 2 — with inlets and outlets placed in alternating rows on the ceiling; Case 3 — with the same size and location of inlets as in Case 2 but with outlets placed on two side walls at the floor level; Case 4 — with perforated air tubes installed above aisles and outlets placed on the ceiling; and Case 5 — with a perforated air tube installed at each level of shelves. With the localized air ventilation method (Case 5), the climate uniformity at the crop canopy was improved compared to the control case (Case 1). The RSD for the averages of air current speed, air temperature, and vapour pressure deficit (VPD) were improved by 14.3%, 0.1%, and 1.0% respectively. • A comprehensive CFD model evaluated internal air flow and climate uniformity in a vertical farm. • Various air distribution systems designs were evaluated for climate uniformity. • CFD model validations with experimental data are presented. • A localized air distribution design resulted in improved and uniform climate over crop canopy.

Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme

On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R2 value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R2 = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure.

Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato

Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.

Into the Shadows and Back into Sunlight: Photosynthesis in Fluctuating Light.

Photosynthesis is an important remaining opportunity for further improvement in the genetic yield potential of our major crops. Measurement, analysis, and improvement of leaf CO2 assimilation (A) have focused largely on photosynthetic rates under light-saturated steady-state conditions. However, in modern crop canopies of several leaf layers, light is rarely constant, and the majority of leaves experience marked light fluctuations throughout the day. It takes several minutes for photosynthesis to regain efficiency in both sun-shade and shade-sun transitions, costing a calculated 10-40% of potential crop CO2 assimilation. Transgenic manipulations to accelerate the adjustment in sun-shade transitions have already shown a substantial productivity increase in field trials. Here, we explore means to further accelerate these adjustments and minimize these losses through transgenic manipulation, gene editing, and exploitation of natural variation. Measurement andanalysis of photosynthesis in sun-shade and shade-sun transitions are explained. Factors limiting speeds of adjustment and how they could be modified to effect improved efficiency are reviewed, specifically nonphotochemical quenching (NPQ), Rubisco activation, and stomatal responses.

Canopy management: the balance between lodging risk and nitrogen use for spring wheat production in the Canadian Prairies

Lodging risk in spring wheat has grown with the introduction of high-yielding cultivars that require high nitrogen (N) inputs to optimize grain yield and protein content. Canopy management (CM) strategies have been used to reduce lodging risk. However, the short growing season and unpredictable moisture supply may limit the use of these strategies in the Canadian Prairies. The objective of this study was to quantify the ability of agronomic practices to modify the early season crop canopy, and hence lodging risk, and to determine how these practices influence N use and final grain N yield. Split-plot field trials were established at four site-years (SYs) in Manitoba to evaluate CM strategies including three plant densities, a split N fertilizer application, and the application of chlormequat chloride (CCC) on canopy size (dry matter (DM) and height at anthesis), N uptake and remobilization, lodging and stalk strength, grain yield, protein, and grain N yield on the cultivar AAC Brandon. Precipitation was low during this study (64%–78% of long-term average (LTA)), resulting in modest yields and low lodging pressure. However, low plant density (PD), split N, and CCC applications all showed potential to reduce lodging risk without reducing grain yield or protein concentration. Split N and plant growth regulator (PGR) applications increased final grain N yield by 11.2% and 2.4%, respectively. Therefore, CM practices have potential in the Canadian Prairies to reduce lodging risk without compromising grain N, but results should be validated in higher precipitation environments and across additional cultivars and growing regions of western Canada.

Are No-Till Herbicide-Free Systems Possible? A Simulation Study

Conservation agriculture (CA) allows farmers to reduce costs and enhance soil health, but tends to increase weed infestation and associated crop yield loss, and/or herbicide use. We investigated how much tillage reduces weed infestation and yield loss, and which systems and weed species are the most affected by tillage suppression. We collected farming practices on 395 arable cropping systems mainly from France, and simulated them over 30 years and with 10 weather repetitions, using FlorSys. This process-based model simulates daily multi-species weed floras and crop canopies from cropping systems and pedoclimate over the years. Three series were simulated, (1) using the recorded systems, (2) eliminating tillage without any other changes, (3) eliminating herbicides without any other changes. Each series was run twice, once with a regional weed-flora pool to simulate weed dynamics and their impact on crop production and biodiversity, and once without weeds to predict potential crop yield. Among the recorded systems, herbicide treatment frequency index (HTFI) averaged over rotation increased when tillage frequency decreased. No recorded no-till system was herbicide-free. The untilled crops with the lowest HTFI (0.16) were unusual crops, i.e., relay grass-crops or multi-annual crops. Simulations of the recorded systems showed no correlation between tillage-frequency, and weed biomass or weed-caused yield loss. When tillage was deleted without any other change, yield loss almost doubled. We identified (1) which weed species and traits (e.g., spring annuals) increased after tillage suppression and in which cropping systems, (2) which recorded systems were robust to tillage suppression in terms of yield loss (with cover crops, summer crops, etc.), (3) which no-till systems (recorded or obtained after deleting tillage) limited yield loss (e.g., with frequent and/or efficient herbicides), (4) which management techniques were associated with a reduction in tillage, in herbicides, and in yield loss (long and diverse rotations, cover crops, etc.). No tested system achieved all three objectives simultaneously. The simulations indicated that two CA pillars (diverse crop rotations, cover cropping) were essential to manage weeds while reducing (or eliminating) both tillage and herbicide use. More no-till cropping systems must be investigated to determine whether sustainable no-till herbicide-free systems are possible.

Chlorophyll detector development based on snapshot-mosaic multispectral image sensing and field wheat canopy processing

To achieve rapid nondestructive detection of chlorophyll content in crops in a field environment, we designed a new lightweight device based on a snapshot-mosaic imaging sensor given the sensitive properties of chlorophyll in the red-edge to near-infrared band (700–900 nm) and the advantages of imaging spectroscopy in environmental noise separation. The hardware part of the device includes a spectral camera based on a snapshot-mosaic sensor, a main control unit, a network module, a storage module, a power supply module, and a display and remote-control device. The software part was written using the Qt library and the C++ programming language. It includes the connection and initialization of the sensor, the control of the exposure time, the display of the image in real time, the acquisition and storage, and the calculation of the reflectance. The sensor was tested and calibrated to evaluate the sensor performance. Experimental results show that the different channels of the sensor have an excellent linear relationship for light intensity changes and can be used to measure reflected radiation from crop leaves in a field environment after calibration. At the same time, field experiments on wheat were conducted. A dark channel filtering method for 25 channels of the developed device was proposed to effectively eliminate the scattered light interference from the crop canopy in the spectral images. The background interference was effectively eliminated by background segmentation. Partial least squares regression (PLSR) method was used for modeling after preprocessing and band screening of the spectral data. The model achieved high accuracy with RC2 of 0.87, RV2 of 0.79, and RMSE of 3.94 mg/L. As a result, the system combined the proposed model with the developed device can effectively eliminate the interference of soil background and scattered light caused by the complex structure of crop canopy, and improve the accuracy of diagnosis. So that the system has potential in the crop growth variation analysis.

Optimal design for solar greenhouses based on canopy height

The front roof of the solar greenhouse captures more solar radiation energy, the height of the rear wall exposed to direct sunlight is high and the last row of crop canopy receives direct sunlight throughout the day is the basis for the efficient use of solar thermal resources in solar greenhouses, however, previous studies generally focused on the optimization of individual structural parameters of greenhouses, never considering the greenhouse structure-crop cultivation correlation. Therefore, a model for optimal the design of the structural dimensions of the solar greenhouse based on the canopy height was established, comparing the calculated values with the actual structural dimensions of greenhouse and analyzing their structural stability in Abaqus software. The results showed that: The calculated values of the model and the actual construction parameters are in good agreement and meet the structural stability requirements; Taking the 12 m span of the solar greenhouse in Xi'an area as an example, the difference in solar radiation capture on the winter solstice for greenhouse front roofs of different structures under three canopy height conditions were small, 195.95, 194.76 and 192.44 MJ/m, respectively. Taking the 8 m span solar greenhouse in Harbin area as an example, when the canopy height is reduced from 2.0 m to 0.8 m, the corresponding total cost of solar greenhouse construction can be reduced by 5.24%, of which the wall and skeleton costs are reduced by 12.54% and 5.08%, respectively. This study can provide a reference for the optimization of structural dimensions of the solar greenhouse.

Tolerance of plasticulture strawberry to 2,4-D choline applied to row middles

AbstractField studies in strawberry grown on polyethylene-mulched raised beds were conducted from 2018 to 2019 and 2019 to 2020 in Clayton, NC, to determine ‘Camarosa’ and ‘Chandler’ strawberry tolerance to 2,4-D directed to the row middle between beds. Treatments included 2,4-D at 0, 0.53, 1.06, 1.60, and 2.13 kg ae ha−1 applied alone and sequential treatments (0.53 followed by [fb] 0.53 or 1.06 fb 1.06 kg ae ha−1). Initial treatments were applied in winter (December 2018 or January 2020) during vegetative growth, and sequential applications were applied in spring (April 2019 or March 2020) during reproductive growth. No differences among treatments were observed for visual foliage injury, strawberry crop canopy, fruit yield, and fruit quality (pH, titratable acidity, and soluble solid content).

How Soil Covers Can Affect the Dynamics of Temperature, Soil Moisture, and Productivity of Creeping Fresh Market Tomatoes?

The aim was to evaluate the influence of soil covers on the dynamics of soil temperature and moisture, canopy air temperature, and yield of creeping fresh market tomatoes. The experiment was carried out in randomized blocks design, using the tomato cultivar Thaise, with 5 treatments, 4 replications, with different soil covers: a) Uncovered soil (conventional planting); b) Mulching (double-sided plastic film - black and white); c) Sorghum; d) Sudan grass and e) Pearl millet), cultivated in Tangará da Serra, Mato Grosso, Brazil. Soil temperature was monitored at depths of 5, 10, 20, and 30 cm and in the crop canopy, using thermocouple sensors of the type "K". Soil moisture was monitored in the 0 to 30 cm layer, using of time-domain reflectometry (TDR) probes. Soil temperature and moisture were evaluated throughout the cycle and, in the end, the total and commercial yield of tomato crop. Soil covers have a positive influence on soil temperature and moisture in the cultivation of creeping fresh market tomatoes so that soil cover with mulching provides the highest soil temperature in the early stages of development and covers with mulching and pearl millet provide the highest values of soil moisture. The highest total and commercial yield of tomato were in the soil cover with mulching, with 110.71 and 75.93 t ha-1, respectively, presenting ideal ranges of temperature and soil moisture, so that the other treatments do not differ from each other, with the average total yield of 91.45 t ha-1.

Alteration in surface energy balance fluxes induced from long-term disk-tilled versus no-till management in maize production

Impacts of tillage-induced surface conditions on modification of the soil-crop-atmospheric interface have not been adequately studied. There is poor quantitative understanding at the intersection of tillage and surface energy balance (SEB), especially for the complete growing season. This research measured and evaluated SEB fluxes, i.e., net radiation (Rn), latent heat (LE), sensible heat (H), and ground heat flux (G) for three growing seasons (2011–2013) in a concurrent fashion in two similarly-managed maize (Zea mays L.) fields, which were under disk-till (DT) and no-till (NT) management for 17 years. To this end, each field was equipped with an identical Bowen Ratio Energy Balance System (BREBS) that collected 30-minute SEB data throughout the growing season. G was the most impacted SEB component, followed by H, LE, and Rn on an average basis, although these comparisons were subject to change during different times in the growing season. Seasonal mean interpretations masked within-season heterogeneity in DT vs. NT SEB dynamics and thus, daily differences for all SEB components, albedo (α), surface soil moisture (θv), and Bowen Ratio (β) were quantified. Greater α in NT field (0.16) was maintained throughout the growing season than DT field (0.14). All SEB components demonstrated greater differences between DT and NT in the early and late growing season. Nevertheless, SEB observations show that tillage impacts during significant crop canopy cover should not be ignored. NT surface (0–0.30 m) θv was as much as 22% greater than that of DT, and held critical importance for regional NT crop production during the extreme 2012 drought. θv also exerted control on albedo, with wetter soils having a lower albedo, which consequently translated into altered Rn across DT and NT fields. The research findings shown here underscore the particular importance of accounting for within-season behavior of albedo and G along with other components of SEB for evaluating soil management-related applications.

India’s Greening Trend Seems to Slow Down. What Does Aerosol Have to Do with It?

Multiple drivers perturb the terrestrial carbon cycle, which ultimately reshapes the fertilization of carbon dioxide (CO2) and reorientates the climate. One such driver is atmospheric aerosols, which cascade the ecosystem’s productivity in a large proportionality. Investigating this relation is non-conventional and limited across the globe. With the abundance of heterogenetic terrestrial ecosystems, India’s primary productivity has a large proportion of the global carbon balance. Under climate change stress, India’s unique spatial and climatological features perturb atmospheric aerosols from natural sources to anthropogenic sources. In light of that, this study utilizes the Carnegie–Ames Stanford Approach (CASA) model to elucidate the consequence by examining the potential effect of aerosol load on the ecosystem productivity (Net Primary Production; NPP) for various agroclimatic zones of India from 2001–2020. CASA reveals a negative decadal amplitude with an overall increase in the NPP trend. In contrast, aerosol loadings from MODIS highlight the increasing trend, with definite seasonal intensities. Employing the CASA model and earth observations, the study highlights the increase in NPP in forest-based ecosystems due to relatively lower aerosols and higher diffuse radiation. Critically, strong dampening of NPP was observed in the agroecological and sparse vegetation zones inferring that the aerosol loadings affect the primary productivity by affecting the photosynthesis of canopy architecture. Spatial sensitivity zones across different ecological regions result in a non-homogenous response because of different phenological and canopy architecture that is mediated by the radiation intensities. Based on the analysis, the study infers that AOD positively influences the canopy-scale photosynthesis by diffuse radiation, which promotes NPP but is less likely for the crop canopy ecosystems. Barring the limitations, enhancement of NPP in the forest ecosystems offset the demand for carbon sink in the agroecosystems. Findings from this study reveal that a more precise provenance of aerosol effects on carbon fluxes is required to understand the uncertainties in the terrestrial carbon cycle.

The Smart Agriculture based on Reconstructed Thermal Image

The utilization of thermal image in supporting precision agriculture is tremendous nowadays. There are many applications of thermal images in agricultural fields, such as detecting crop water stress, monitoring of free-range rabbits, measuring of crop canopy temperature and so on. Furthermore, the importance of thermal camera became the urgent need of perform the smart agriculture. Otherwise, the price of thermal camera is very expensive todays. Then, this kind of camera is not easy to find in the market. Therefore, it makes the utilization of implementation thermal images difficult. In order to handle this problem, the proposed method intends to generate thermal image from visible images. Further, the thermal information concerning with the agriculture, especially the fertility of leaves in paddy fields and the water stress can be monitored. The proposed method uses deep learning architecture to learn the thermal and visible image dataset. It applies Generative Adversarial Network architecture. This GAN pre-trained model trained using 150 images of training dataset and tested using many images of testset. The obtained model is used for generating thermal images from visible images. The results show the constructed thermal image has high accuracy. The assessment metric uses SSIM and PSNR methods. Their indexes show that the results have the high accuracy. The visual assessment shows the reconstructed thermal images also have high precision. Finally, the constructed thermal images can be implemented in smart agriculture purposes.

A novel artificial bee colony-optimized visible oblique dipyramid greenness index for vision-based aquaponic lettuce biophysical signatures estimation

In response to the challenges in providing real-time extraction of crop biophysical signatures, computer vision in computational crop phenotyping highlights the opportunities of computational intelligence solutions. Shadow and angular brightness due to the presence of photosynthetic light unevenly illuminate crop canopy. In this study, a novel vegetation index named artificial bee colony-optimized visible band oblique dipyramid greenness index (vODGIabc) was proposed to enhance vegetation pixels by correcting the saturation and brightness levels, and the ratio of visible RGB reflectance intensities. Consumer-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life cycle. The introduced saturation rectification coefficient (Ω), value rectification coefficient (ν), green–red wavelength adjustment factor (α), and green–blue wavelength adjustment factor (β) on the original triangular greenness index resulted in 3D canopy reflectance spectrum with two oblique tetrahedrons formed by connecting the vertices of visible RGB band reflectance and maximum wavelength point map to corresponding saturation and value of lettuce-captured images. Hybrid neighborhood component analysis (NCA), minimum redundancy maximum relevance (MRMR), Pearson’s correlation coefficient (PCC), and analysis of variance (ANOVA) weighted most of the canopy area, energy, and homogeneity. Strong linear relationships were exhibited by using vODGIabc in estimating lettuce crop fresh weight, height, number of spanning leaves, leaf area index, and growth stage with R2 values of 0.936 8 for InceptionV3, 0.957 4 for ResNet101, 0.961 2 for ResNet101, 0.999 9 for Gaussian processing regression, and accuracy of 88.89% for ResNet101, respectively. This low-cost approach on developing greenness index for biophysical signatures estimation proved to be more accurate than the previously established triangular greenness index (TGI) using RGB smartphone camera.

Leaf Pigments, Surface Wax and Spectral Vegetation Indices for Heat Stress Resistance in Pea

Pea is a grain legume crop commonly grown in semi-arid temperate regions. Pea is susceptible to heat stress that affects development and reduces yield. Leaf pigments and surface wax in a crop canopy make the primary interaction with the environment and can impact plant response to environmental stress. Vegetation indices can be used to indirectly assess canopy performance in regard to pigment, biomass, and water content to indicate overall plant stress. Our objectives were to investigate the contribution of leaf pigments and surface wax to heat avoidance in pea canopies, and their associations with spectral vegetation indices. Canopies represented by 24 pea cultivars varying in leaf traits were tested in field trials across six environments with three stress levels in western Canada. Compared with the control non-stress environments, heat stress reduced leaf lamina and petiole chlorophyll a, chlorophyll b, and carotenoid concentrations by 18–35%, and increased leaf lamina chlorophyll a/b ratio, anthocyanin and wax concentrations by 24–28%. Generally, greater leaf pigment and wax concentrations were associated with cooler canopy temperature and high heat tolerance index (HTI) values. Upright cultivars had higher HTI values, whereas the lowest HTI was associated with normal leafed vining cultivars. Vegetation indices, including photochemical reflectance index (PRI), green normalized vegetation index (GNDVI), normalized pigment chlorophyll ratio index (NPCI), and water band index (WBI), had strong correlations with HTI and with heat avoidance traits. This study highlights the contribution of pigments and wax as heat avoidance traits in crop canopies, and the potential application of spectral measurements for selecting genotypes with more heat resistant vegetation.

Estimating Bulk Stomatal Conductance in Grapevine Canopies

In response to changes in their environments, grapevines regulate transpiration using various physiological mechanisms that alter conductance of water through the soil-plant-atmosphere continuum. Expressed as bulk stomatal conductance at the canopy scale, it varies diurnally in response to changes in vapor pressure deficit and net radiation, and over the season to changes in soil water deficits and hydraulic conductivity of both the soil and plant. To help with future characterization of this dynamic response, a simplified method is presented for determining bulk stomatal conductance based on the crop canopy energy flux model by Shuttleworth and Wallace using measurements of individual vine sap flow, temperature and humidity within the vine canopy, and estimates of net radiation absorbed by the vine canopy. The methodology presented respects the energy flux dynamics of vineyards with open canopies, while avoiding problematic measurements of soil heat flux and boundary layer conductance needed by other methods, which might otherwise interfere with ongoing vineyard management practices. Based on this method and measurements taken on several vines in a non-irrigated vineyard in Bordeaux France, bulk stomatal conductance was estimated on 15-minute intervals from July to mid-September 2020 producing values similar to those presented for vineyards in the literature. Time-series plots of this conductance show significant diurnal variation and seasonal decreases in conductance associated with increased vine water stress as measured by predawn leaf water potential. Global sensitivity analysis using non-parametric regression found transpiration flux and vapor pressure deficit to be the most important input variables to the calculation of bulk stomatal conductance, with absorbed net radiation and bulk boundary layer conductance being much less important. Conversely, bulk stomatal conductance was one of the most important inputs when calculating vine transpiration, emphasizing the usefulness of characterizing its dynamic response for the purpose of estimating vine canopy transpiration in water use models.

Hyperspectral vegetation indices offer insights for determining economically optimal time of harvest in Mentha arvensis

Continual crop monitoring and harvesting at optimum time to ensure maximum economic returns is a huge challenge for farmers having small landholdings in Indo-Gangetic plains where Mentha arvensis is a popular cash crop. The present study evaluates different hyperspectral indices for phenotyping the canopy of M. arvensis winter crop (cv. CIM-Kranti) for detection of optimum harvest time using ground-based plant traits. Field experiments were conducted during mid-crop growth stage to late crop growth period for two consecutive years using hyperspectral camera (400–940 nm) to acquire images representing the spectral reflectance of crop canopies. Hyperspectral vegetation indices (HVIs) derived from these images were further evaluated with an aim to identify those hyperspectral indices showing a strong correlation to measured crop parameters. Out of the 18 HVIs, 4 HVI’s i.e. photochemical reflectance index (PRI), carotenoids reflectance index (CRIr), hyperspectral normalized difference vegetation index (HNDVI) and plant senescence reflectance index (PSRI) were recognized as potential predictors for sucker traits (weight and length) and thereby indicators of optimum harvesting time for M. arvensis winter crop (cv. CIM-Kranti) cultivated primarily to produce suckers for crop propagation. Our results show promise for the development of non-destructive methods for the detection of optimum harvest time using crop reflectance. This study holds importance for the menthol mint farmers, who cultivate the crop for suckers during winter months for additional income generation.

An insight into the sensitivity of fully polarimetric SAR data to biomass of pearl millet crop

The development of polarimetric decomposition techniques improves the capability to understand the complex scattering mechanisms occurring in the dryland crops. The present study is focused on assessing the sensitivity of coherent, incoherent, model and Eigen vector-based decomposition techniques from multitemporal Radarsat-2 polarimetric dataset to scattering mechanisms occurring in the critical growth stages of pearl millet crop. Yamaguchi volume component along with its temporal response was observed to be sensitive and reliable due to its dynamic biomass characterization. Overestimation of volume component and earlier saturation of biomass from 4 kgm−2 were observed limitations in Freeman 2 & 3 component decomposition when dealing with higher vegetation crops. The decrease in volume scattering over increasing biomass proved Van Zyl response contrary to the established understanding. Panicle stage was identified as the critical stage for discriminating pearl millet from similar structured maize and sorghum crops. In this stage, alpha angle generated from Cloude-Pottier decomposition almost reached 550 indicating the increase in contribution of dihedral scattering due to vertical prominent panicles in the pearl millet fields. The various Touzi parameters (α1, Ψ1, φ1 and τ1) were observed to have sensitivity towards changes in crop canopy structures, which will require further research to verify. The proposed Δ(F3-Y4) was the most suitable Volume Difference Index (VDI) for characterizing biomass of erectophile crops as the dynamic biomass response was found even beyond 8 kgm−2.

Monitoring Directional Dynamics of Growing Wheat Crop Canopy Using Ground based Time Series Remote Sensing Radiative Measurements

Agricultural crop monitoring is an issue of extreme importance under global climate change, increased natural disasters and population explosion threatening global food security. In this paper, dynamic behaviour of spectro-directional reflectance properties of wheat canopy has been studied using radiometric measurements performed over a wheat field during the entire crop life cycle under varying viewing geometries. The study reveals that biological growth rhythm of wheat crop associated with continuous alteration in canopy condition particularly in terms of optical and morphological properties during the life cycle results in a distinct and systematic changing pattern of bidirectional responses in red and near Infrared (NIR). Analysis shows appreciable sensitivity of radiometric measurements both in the red and NIR regions to crop phenological transformation and changes. Soil background influenced the overall angular anisotropy pattern and manifested relatively high surface reflectance specifically at the early growth stage. At this stage, changes in viewing direction give rise to additional variability in directional reflectance due to varying proportion of soil-vegetation and enhanced contrast between wheat crop and its soil background. Both amplitude and angular pattern of directional response of the canopy undergo appreciable changes with time. Asymmetry in directional reflectance has been noticed in both the spectral regions on either side of nadir in the principal plane. In this connection, time varying angular characteristics of normalized difference vegetation index (NDVI) has also been studied in relation to crop growth.