Wheat Canopy Research Articles

Assessing the combined effects of mechanical and chemical weeding on weed dynamics in winter wheat

AbstractMechanical weeding, such as harrowing, offers a promising approach for reaching the European Union's goal of a 50% reduction in pesticide use. To assess its potential, used alone or with foliar‐applied herbicides without residual activity, a 6‐year study was conducted on winter wheat cultivated in loamy soil under temperate conditions (Belgium). Weed density dynamics and percentage weed control (WC) were measured and compared between wheat tillering (BBCH 27‐29) and wheat canopy closure (BBCH 39‐75). Weeds were categorised as weeds maturing generally synchronously with wheat (OW), newly emergent (NE) weeds, or new weed species (NS) appearing in spring. The presence of OW was negatively correlated with yields (up to −0.44), while spring weeds had low or no impact on yield. Overall, one harrow pass reduced OW pressure significantly, with no statistical differences between one versus two passes (the second pass was performed several days after the first). The percentage of WC reached 92%–94% when herbicide was used in combination with harrowing (respectively for one and two harrow passes), and proved to be more effective than harrowing alone whose efficacy reached 64%–70% (respectively for two and one harrow passes). This study also found evidence effects of natural competition from wheat on weeds. Specifically, 54% WC was observed just after harrowing, and 70% was observed when the wheat canopy was closed. These findings suggest that an integrated weed management approach may involve an initial harrowing pass with subsequent evaluation of its effectiveness prior to commencing another intervention. In the event of poor efficacy, a full dose application of herbicide may be warranted.

MSCVI: An improved algorithm for mitigating LiDAR noise and occlusion effects in field wheat tiller number calculation

Tiller number is a key agronomic trait of the crop population, reflecting the adaptability of crop to the environment and the status of plant growth, as well as grain yield. As a state-of-the-art form of active remote sensing that penetrates the vegetation canopy and provides a detailed representation of 3D structures, terrestrial laser scanning (TLS) is beginning to show great potential in precisely counting the tiller number. However, current research of TLS-derived wheat canopy tiller number is commonly affected by mutual occlusion among plants and noise problem. In this study, we proposed a novel Mean Shift Clustering algorithm based on Voxel Interpolation (MSCVI) to effectively mitigate those effects by removing excess noise and interpolating unsampled voxels. The findings demonstrated that there was a strong exponential relationship between the gap fraction (Pgap) and point cloud density for wheat plots. In addition, MSCVI was effective to count the tiller number of wheat under different field treatments (R2 = 0.69, RMSE = 79 tilllers/m2), producing better results than previous adaptive layering and hierarchical clustering (ALHC) algorithm. MSCVI could obtain more precise and detailed wheat canopy information by denoising and compensating the point cloud data, which greatly improved the accuracy of detecting tiller numbers under the condition of high plant density, planophile plant type and tiller stage data. This study provides new insights into effectively mitigating noise and occlusion between plants and within dense canopies, and has potential for accurate calculation of the tiller number in the assessment of crop yield phenotype.

Early diagnosis of wheat powdery mildew using solar-induced chlorophyll fluorescence and hyperspectral reflectance

Powdery mildew disease threatens wheat production worldwide, and early detection is of great significance for disease control and maximizing yield and quality. To improve early remote sensing detection of wheat powdery mildew, solar-induced chlorophyll fluorescence (SIF) parameters were extracted using three-band Fraunhofer line discrimination (3FLD) and reflectance index approaches, and vegetation index (VI) was calculated by hyperspectral reflectance. All features and feature subsets of different data sources were used as inputs to multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms to construct a wheat powdery mildew monitoring model. SVM includes linear kernel function (LK), polynomial kernel function (PK), and Gaussian radial basis function (RBF). Under wheat powdery mildew stress, wheat canopy reflectance showed a blue shift, and fluorescence weakened. The correlation between SIF−A intensity and disease index (DI) in the O2−A band extracted using the 3FLD method was the highest at −0.781, showing that the SIF parameter was useful for monitoring powdery mildew. Whether based on all features or feature subsets, the RBF model achieved the highest model accuracy, followed by the RF and the MLR. In the feature subset, the accuracy ranges of RBF, LK, and PK models are 0.740−0.871, 0.724−0.850, and 0.716−0.841 respectively. The SIF+VI in the RBF model is more useful for early and stable disease monitoring of wheat powdery mildew. This innovative technical solution is expected to support the early diagnosis of wheat powdery mildew, significantly improving disease prevention and control efficiency and effectiveness.

Estimation on powdery mildew of wheat canopy based on in-situ hyperspectral responses and characteristic wavelengths optimization

Wheat powdery mildew (WPM) mainly damages leaves, which leads to yield reduction. Although spectroscopy has been considered as one of the effective methods to detect crop diseases, the severity estimation of WPM is challenged by complex changes of wheat canopy reflection, wavelength redundancy in hyperspectral data, et al., so that to reduce the accuracy of disease index (DI) estimation. Aiming to estimate DI of WPM, an optimization method combined of band intervals and sensitive wavelengths screening was proposed by in-situ reflected spectral responses. Field experiments were conducted in 2022 and 2023. With regards to data processing, after preprocessing by Savitzky-Golay (SG) and multiplicative scatter correction (MSC) methods, the backward interval partial least squares (Bipls) method was used to featuring band interval. Then, the characteristic wavelengths in the selected interval were optimized by the variable important in projection (VIP), maximal information coefficient (MIC) and variable importance measure based on random forest (RFVIM) method, separately. Finally, partial least squares regression (PLSR) was used to establish the DI estimating model of WPM, and the best model was selected by comparing the performance among the models. The results indicate that the Bipls-VIP-PLSR model has the best performance (2022: =0.78, RMSEV =10.94%; 2023: =0.75, RMSEV=11.73%), and it was used to estimate the DI changes in the field. The distribution map of DI provided a potential for WPM monitoring and plant protection decision-making.

Quantitative Analysis of Vertical and Temporal Variations in the Chlorophyll Content of Winter Wheat Leaves via Proximal Multispectral Remote Sensing and Deep Transfer Learning

Quantifying the vertical distribution of leaf chlorophyll content (LCC) is integral for a comprehensive understanding of the physiological status and function of winter wheat crops, having significant implications for crop management and yield optimization. In this study, we investigated the vertical LCC trait of winter wheat during two consecutive field growth seasons using proximal multispectral imaging measurements to evaluate vertical variations of LCC within winter wheat canopies. The results revealed the non-uniform vertical LCC distribution varied across the entire growth season. The effects of nitrogen fertilization rate on LCC among vertical layers increased gradually from upper to lower layers of canopy. To enhance LCC prediction accuracy, this study proposes a deep transfer learning network model for leaf trait estimation (LeafTNet). It integrates the advantages of physical radiative transfer simulations with deep neural network through transfer learning. The results demonstrate that the LeafTNet achieved remarkable predictive performance and strong robustness. Furthermore, the proposed method exhibits superior estimation accuracy compared to empirical statistical method and traditional machine learning method. This study highlights the performance of LeafTNet in accurately and efficiently quantifying LCC from proximal multispectral data, which provide technical support for the estimation of the vertical distribution of leaf traits and improve crop management.

Improving detection of wheat canopy chlorophyll content based on inhomogeneous light correction

Effectively improving the detection accuracy of wheat chlorophyll content is of great significance for the detection of photosynthetic capacity and growth status of wheat canopy. However, due to inhomogeneous light distribution issues in canopy, the existence of shaded and sun wheat leaves in wheat canopy has influence for spectral-based detection of chlorophyll content. Therefore, in order to improve detection of wheat canopy chlorophyll content, a light distribution correction method was proposed to correct intact leaves’ light distribution based on shaded and sun leaves in multispectral images. Firstly, R-G-NIR images were reconstructed to segment and analyze shaded and sun leaves of wheat. Secondly, Homomorphic Filter (HF) and Gamma light correction method was used to optimize shaded leaves’ light distribution. Then, the differential responses of 10 different types of vegetation indices in shaded, sun, original intact and corrected intact leaves were analyzed to screen chlorophyll-sensitive parameters based on Random Frog Method (RFM). Finally, Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR) were used to establish models for the detection of chlorophyll content in shaded, sun, original intact and corrected intact leaves of wheat, respectively. The results showed that the proposed light correction method reduced the inhomogeneous light and kept more uniform. Normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), difference vegetation index (DVI), normalized redness intensity (NRI), and optimized soil-adjusted vegetation index (OSAVI) were selected as the optimal spectral variables. And the models after correction had higher accuracy than the models before correction. The wheat chlorophyll content model of corrected intact leaves based on RF had the highest accuracy, with a calibration set Rc2 of 0.816, RMSEc of 3.702, a validation set Rv2 of 0.804, RMSEv of 3.958, respectively. The research integrates the above results to improve detection of wheat canopy chlorophyll content, which provides technical support for the light distribution correction in multispectral images.

基于实时光谱信息的小麦变量施肥效率分析与评价

Studying the effect of variable fertilization during the jointing stage on winter wheat production in the rice-wheat rotation area is critical for evaluating the application effect and economic benefits of variable fertilization technology. The variable fertilization experiment of wheat was carried out in Jiangsu province by using the self-developed fertilizer applicator. Three fertilization methods were used to conduct a comparative analysis of the fertilization amount, population structure, and yield of winter wheat during the jointing stage. On this basis, the economic feasibility of variable fertilization during the jointing stage was evaluated. The experimental results showed that the control accuracy of variable-rate fertilization with fertilization equipment was greater than 95%. After variable fertilization, the coefficient of variation of NDVI values in the winter wheat canopy spectral data remained between 0.076 and 0.125, and the Christensen uniformity coefficient remained between 0.901 and 0.940. Compared with the traditional empirical balance method for quantitative fertilization of plots, the real-time variable fertilization plot used 13.6 kg/hm2 less fertilizer during the jointing stage. The findings validate that implementing variable fertilization can help reduce nitrogen fertilizer input, improve nitrogen fertilizer utilization efficiency, reduce environmental pollution, and enhance the sustainability of agricultural production.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Winter Wheat Mapping Method Based on Pseudo-Labels and U-Net Model for Training Sample Shortage

In recent years, the semantic segmentation model has been widely applied in fields such as the extraction of crops due to its advantages such as strong discrimination ability, high accuracy, etc. Currently, there is no standard set of ground true label data for major crops in China, and the visual interpretation process is usually time-consuming and laborious. The sample size also makes it difficult to support the model to learn enough ground features, resulting in poor generalisation ability of the model, which in turn makes the model difficult to apply in fine extraction tasks of large-area crops. In this study, a method to establish a pseudo-label sample set based on the random forest algorithm to train a semantic segmentation model (U-Net) was proposed to perform winter wheat extraction. With the help of the GEE platform, Winter Wheat Canopy Index (WCI) indicators were employed in this method to initially extract winter wheat, and training samples (i.e., pseudo labels) were built for the semantic segmentation model through the iterative process of “generating random sample points—random forest model training—winter wheat extraction”; on this basis, the U-net model was trained with multi-time series remote sensing images; finally, the U-Net model was employed to obtain the spatial distribution map of winter wheat in Henan Province in 2022. The results illustrated that: (1) Pseudo-label data were constructed using the random forest model in typical regions, achieving an overall accuracy of 97.53% under validation with manual samples, proving that its accuracy meets the requirements for U-Net model training. (2) Utilizing the U-Net model, U-Net++ model, and random forest model constructed based on pseudo-label data for 2022, winter wheat mapping was conducted in Henan Province. The extraction accuracy of the three models is in the order of U-Net model > U-Net++ model > random forest model. (3) Using the U-Net model to predict the winter wheat planting areas in Henan Province in 2019, although the extraction accuracy decreased compared to 2022, it still exceeded that of the random forest model. Additionally, the U-Net++ model did not achieve higher classification accuracy. (4) Experimental results demonstrate that deep learning models constructed based on pseudo-labels exhibit higher classification accuracy. Compared to traditional machine learning models like random forest, they have higher spatiotemporal adaptability and robustness, further validating the scientific and practical feasibility of pseudo-labels and their generation strategies, which are expected to provide a feasible technical pathway for intelligent extraction of winter wheat spatial distribution information in the future.

Efecto del uso de boquillas de aire inducido y adyuvante sobre la calidad de aplicación de fitosanitarios

Spraying of phytosanitary products must ensure efficient pest control while avoiding losses that could damage human health and environment. The objective of the study was to evaluate the effect of conventional hollow cone and air-induced hydraulic nozzles, and the addition of an adjuvant (Methylated Seed Oil [MSO]) upon the application quality of a fungicide (penetration, distribution, and uniformity) and losses due to endodrift on wheat and soybean canopies. In each crop, the experimental design was completely randomized,with four replications and a factorial arrangement of nozzles x adjuvant. Droplet spectrum was evaluated by using water-sensitive cards, and brilliant blue as a tracer in the application mixture. According to the results, the MSO did not homogenize the distribution of droplets generated by air-induced nozzles in the wheat canopy, as it depended on the crop height evaluated. In addition, the MSO reduced endodrift independently of the type of nozzle used. Thedistribution of droplets on the soybean canopy depended on the height and not on the type of nozzle nor the addition of MSO. The MSO and its combination with the nozzle type did not reduce endodrift in this crop.

Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions.

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

Examining the Percent Canopy Cover and Health of Winter Wheat in No-Till and Conventional Tillage Plots Using a Drone

The percent canopy cover and health of winter wheat are important crop performance indicators. Thus, understanding how tillage management practices affect these indicators is beneficial for improving crop performance and consequently yield. The availability of high-resolution drone data with spectral characteristics provides an opportunity to examine the percent canopy cover and health of winter wheat in different tillage systems. This is because the use of drones provides real-time high spatial resolution and temporal images to effectively monitor winter wheat conditions throughout the growing season. Nonetheless, very limited studies have utilized drone data for assessing the percent canopy cover and health conditions of winter wheat for different tillage practices. This study aimed to examine the percent canopy cover and health of winter wheat in no-till and conventional tillage plots using a drone. We used the mean Normalized Difference Vegetation Index (NDVI) ± Standard Deviation (SD) (0.89 ± 0.04) of winter wheat for the growth stages of tillering, jointing, and boot/heading to generate the percent wheat canopy cover. The Normalized Difference Red-Edge (NDRE) produced for winter wheat at the middle and late growth stages was used as a proxy for wheat health condition. We found that the mean percentage canopy cover of winter wheat was about 4% higher in no-till compared to conventional tillage plots in most of the growing season. The mean NDRE ± standard error (SE) of winter wheat was about 0.44 ± 0.01 and 0.43 ± 0.01 for no-till and conventional tillage plots, respectively, during the mid- and late growth stages. There was no significant difference in either the percent canopy cover or health of winter wheat between no-till and conventional tillage plots. The results generated in this study could be used to support farmers’ decision-making process regarding tillage practices and wheat crop performance.

Estimating wheat spike-leaf composite indicator (SLI) dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield (GY) has been overlooked in the accurate spectral prediction of yield. Thus, it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis. Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years, our objectives were to (i) construct a yield-related phenotypic trait (spike–leaf composite indicator, SLI) accounting for the contribution of the spike to photosynthesis, (ii) develop a novel spectral index (enhanced triangle vegetation index, ETVI3) sensitive to SLI, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms. The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index. ETVI3 maintained a strong correlation with SLI throughout the growth stage, whereas the correlations of other spectral indices with SLI were poor after spike emergence. Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI, with the most accurate estimates of SLI showing coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71, 0.047, and 26.93%, respectively. These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY. This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Effects of long-term positioning tillage on13C assimilate distribution and grain yield formation in wheat

To provide a theoretical basis and technical support for the high-yield and high-efficiency production of wheat, we examined the effects of different tillage patterns on wheat grain yield of Jimai 22 and the physiological mechanisms in an experiment with three treatments: 14 years in rotary tillage (R), minimal and no tillage (S), and minimal and no tillage with a 2-year subsoiling interval (SS). We assessed the light interception by wheat plant canopy, the distribution of photosynthate transport, and grain yield for the three cultivation modes. The results showed that leaf area index was significantly higher for SS treatment than the other treatments at 14-28 days after anthesis. The interception rate and amount of photosynthetically active radiation in the upper and middle layers of wheat canopy were significantly higher for SS treatment than R and S treatments at 21 days after anthesis. The contribution rate of grain assimilation and the distribution proportion of 13C assimilated in grain, and the maximum and average filling rates, were the highest under SS treatment. The 1000-kernel weight for SS treatment increased by 8.7% and 9.6%, and the grain yield increased by 14.2% and 19.4% compared with R and S treatments, respectively. SS treatment significantly improved light energy utilization by wheat canopy, promoted the accumulation and transport of dry matter, increased the grain-filling rate, increased grain weight, which together contributed to the highest grain yield. Therefore, SS was the optimal tillage pattern under the conditions of this experiment.

Environmental effects and their impact on yield in adjacent experimental plots of high-stem and short-stem wheat varieties

BackgroundIn regional wheat trials, when short-stem wheat varieties and high-stem wheat varieties are planted adjacent to each other in small plots, changes in their marginal plot environment can lead to bias in yield evaluation. Currently, there is no relevant research revealing the degree of their mutual influence.ResultsIn a regional wheat experiment, when high-stem wheat varieties and short-stem wheat varieties were planted adjacent to one another, there was no significant change in soil temperature or humidity in the high-stem wheat variety experimental plot from November to May compared to the control plot, while the soil humidity in the short-stem wheat variety experimental plot was greater than that in the control plot. In May, the soil temperature of the short-stem wheat varieties in the experimental plot was lower than that in the control plot. Illumination of the wheat canopy in the high-stem wheat variety experimental plot had a significant positive effect in April and May, while illumination of the wheat canopy in the short-stem wheat variety experimental plot had a negative effect. The chlorophyll fluorescence parameters of flag leaves in the high-stem wheat variety experimental plots showed an overall increasing trend, while the chlorophyll fluorescence parameters of flag leaves in the experimental plots of short-stem wheat varieties showed a decreasing trend. The analysis of the economic yield, biological yield, and yield factors in each experimental plot revealed that the marginal effects of the economic yield and 1000-grain weight were particularly significant and manifested as positive effects in the high-stem wheat variety experimental plot and as negative effects in the short-stem wheat variety experimental plot. The economic yield of the high-stem wheat variety experimental plot was significantly greater than that of the control plot, the economic yield of the short-stem wheat variety experimental plot was significantly lower than that of the control plot, and the economic yield of the high-stem experimental plot was significantly greater than that of the short-stem experimental plot. When the yield of the control plot of the high-stem wheat varieties was compared to that of the control plot of the short-stem wheat varieties, the yield of the control plot of the short-stem wheat varieties was significantly greater than that of the control plot of the high-stem wheat varieties.ConclusionsBased on these findings, it is concluded that plots with high-stem and short-stem wheat varieties are adjacent in regional wheat trials, the plots of high-stem wheat varieties are subject to marginal positive effects, resulting in a significant increase in economic yield; the plots of short-stem wheat varieties are subject to marginal negative effects, resulting in a decrease in economic yield. This study reveals the mutual influence mechanism of environment and yield with adjacent planting of high-stem and short-stem wheat varieties in regional wheat trials, providing a useful reference and guidance for optimizing the layout of regional wheat trials.

Effects of dense planting patterns on photosynthetic traits of different vertical layers and yield of wheat under different nitrogen rates

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted wheat. Inter-plant and inter-row distances were used to define rate-fixed pattern (RR) and row-fixed pattern (RS) density treatments. Meanwhile, four nitrogen (N) rates (0, 144, 192, and 240 kg N ha−1, termed N0, N144, N192, and N240) were applied with three densities (225, 292.5, and 360 × 104 plants ha−1, termed D225, D292.5, and D360). The wheat canopy was clipped into three equal vertical layers (top, middle, and bottom layers), and their chlorophyll density (ChD) and photosynthetically active radiation interception (FIPAR) were measured. Results showed that the response of ChD and FIPAR to N rate, density, and pattern varied with different layers. N rate, density, and pattern had significant interaction effects on ChD. The maximum values of whole-canopy ChD in the two seasons appeared in N240 combined with D292.5 and D360 under RR, respectively. Across two growing seasons, FIPAR values of RR were higher than those of RS by 29.37% for the top layer and 5.68% for the middle layer, while lower than those of RS by 20.62% for the bottom layer on average. With a low N supply (N0), grain yield was not significantly affected by density for both patterns. At N240, increasing density significantly increased yield under RR, but D360 of RS significantly decreased yield by 3.72% and 9.00% versus D225 in two seasons, respectively. With an appropriate and sufficient N application, RR increased the yield of densely planted wheat more than RS. Additionally, the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns, suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

Microclimatic study under wheat, mustard and chickpea crops in western plain zone of Uttar Pradesh

The present study to quantify the variations in microclimate under wheat, mustard and chickpea crops was conducted at ICAR-Indian Institute of Farming Systems Research, Modipuram, Meerut (U.P.), India. Air temperature, relative humidity, CO2 concentration below and above the canopies of wheat, mustard and chickpea were recorded at hourly interval from 07:30 to 17:30 hours at different heights (0.5 m, 1.0 m and 2.0 m) from the ground. CO2 probes like GMP-343, (Diffusion aspiration) was used to record data of CO2 concentration and HPM-75 probes was used to capture the observation of air temperature and relative humidity. Results revealed that diurnal air temperature was continuously increasing from morning to afternoon hrs and highest air temperature was recorded at 13:30 hrs (afternoon). Thereafter, air temperature decreased and reached to the lowest at 17:30 hrs irrespective of crops and height from the ground. Analysis of diurnal air temperature variations at different height clearly showed that tall statured crop such as wheat and mustard reached higher air temperature regime early (13.30 hrs) compared to short statured crop like chickpea at 15.30 hrs. Highest relative humidity was observed at 07.30 hrs and lowest relative humidity was recorded either at 13:30 hrs or at 15:30 hrs. As per study maximum CO2 concentration was found at 07:30 hrs morning and minimum at 15:30 hrs. The lowest concentration of CO2 (624 ppm) was recorded from the chickpea field and highest from the mustard field (630 ppm) at the same point of observation during vegetative phase of crops.

Non-foliar photosynthesis and nitrogen assimilation influence grain yield in durum wheat regardless of water conditions.

There is a need to generate improved crop varieties adapted to the ongoing changes in the climate. We studied durum wheat canopy and central metabolism of six different photosynthetic organs in two yield-contrasting varieties. The aim was to understand the mechanisms associated with the water stress response and yield performance. Water stress strongly reduced grain yield, plant biomass and leaf photosynthesis, and downregulated C/N-metabolism genes and key protein levels, which occurred mainly in leaf blades. By contrast, higher yield was associated with high ear dry weight and lower biomass and ears per area, highlighting the advantage of reduced tillering and its consequent improvement in sink strength that promoted C/N metabolism at the whole plant level. An improved C metabolism in blades and ear bracts and N assimilation in all photosynthetic organs facilitated C/N remobilisation to the grain and promoted yield. Therefore, we propose that further yield gains in Mediterranean conditions could be achieved by considering the source-sink dynamics and the contribution of non-foliar organs, particularly N assimilation and remobilisation during the late growth stages. We highlight the power of linking phenotyping with plant metabolism to identify novel traits at the whole plant level to support breeding programmes.

Improved Hybrid Preprocessing Technique for Effective Segmentation of Wheat Canopies in Chlorophyll Fluorescence Images

Precision agriculture heavily relies on accurately segmenting wheat canopies from chlorophyll fluorescence (CHF) images. However, these images often face challenges due to inherent noise and illumination variations, primarily induced by the thermal activity of photons emitting a fluorescence effect. The unique nature of fluorescence introduces variations in illumination, especially during the crop's dark adaptation before experimentation. This adaptation aims to capture the full fluorescence effect, starting from minimum fluorescence and progressing to maximum fluorescence. In the initial stages of fluorescence, images tend to appear darker compared to those progressing towards maximum fluorescence. This variability necessitates the development of a sophisticated hybrid approach to eliminate noise and enhance contrast collaboratively, maximizing the benefits derived from CHF images. This paper introduces a novel hybrid preprocessing approach designed to address these challenges. The proposed method integrates five denoising techniques, namely Discrete Cosine Transform, Block Matching-3D, Low-Rank Matrix Approximation, Wiener Filtering, and Median Filtering, to mitigate the impact of noise in CHF images. Simultaneously, two enhancement techniques, Adaptive Histogram Specification and Gamma Correction, are employed to accentuate critical features, compensating for inherent variations in illumination during the fluorescence process. The hybrid preprocessing technique was proposed after analysing different combinations of denoising and enhancement techniques. Through qualitative and quantitative analysis of the results, it was observed that Block Matching-3D denoising with Gamma Correction produced the best output, with an Average PSNR of 0.54 and Average MSE of 0.07. This cascaded approach not only emphasizes noise reduction but also prioritizes the enhancement of crucial information within CHF images. By synergistically combining denoising and enhancement methods, the proposed approach optimizes the overall quality of the images, laying a foundation for improved wheat canopy segmentation. This research contributes a comprehensive and innovative solution to the challenges associated with CHF images in precision agriculture. The proposed hybrid approach holds promise for advancing the accuracy and reliability of wheat canopy segmentation, thereby enhancing the efficacy of precision agricultural practices.

Accurate classification of wheat freeze injury severity from the color information in digital canopy images.

This paper explores whether it is feasible to use the RGB color information in images of wheat canopies that were exposed to low temperatures during the growth season to achieve fast, non-destructive, and accurate determination of the severity of any freeze injury it may have incurred. For the study presented in this paper, we compared the accuracy of a number of algorithmic classification models using either meteorological data reported by weather services or the color gradation skewness-distribution from high-definition digital canopy images acquired in situ as inputs against a reference obtained by manually assessing the severity of the freeze injury inflicted upon wheat populations at three experimental stations in Shandong, China. The algorithms we used to construct the models included in our study were based on either K-means clustering, systematic clustering, or naïve Bayesian classification. When analyzing the reliability of our models, we found that, at more than 85%, the accuracy of the Bayesian model, which used the color information as inputs and involved the use of prior data in the form of the reference data we had obtained through manual classification, was significantly higher than that of the models based on systematic or the K-means clustering, which did not involve the use of prior data. It was interesting to note that the determination accuracy of algorithms using meteorological factors as inputs was significantly lower than that of those using color information. We also noted that the determination accuracy of the Bayesian model had some potential for optimization, which prompted us to subject the inputs of the model to a factor analysis in order to identify the key independent leaf color distribution parameters characterizing wheat freeze injury severity. This optimization allowed us to improve the determination accuracy of the model to over 90%, even in environments comprising several different ecological zones, as was the case at one of our experimental sites. In conclusion, our naïve Bayesian classification algorithm, which uses six key color gradation skewness-distribution parameters as inputs and involves the use of prior data in the form of manual assessments, qualifies as a contender for the development of commercial-grade wheat freeze injury severity monitoring systems supporting post-freeze management measures aimed at ensuring food security.