Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel-2 NDVI time-series images in an organic farming system

The study investigates the suitability of time series Sentinel-2 NDVI-derived maps for the subfield detection of a sunflower crop cultivated in an organic farming system. The aim was to understand the spatio-temporal behaviour of subfield areas identified by the K-means algorithm from NDVI maps obtained from satellite images and the ground yield data variability to increase the efficiency of delimiting management zones in an organic farming system. Experiments were conducted on a surface of 29 ha. NDVI time series derived from Sentinel-2 images and k-means algorithm for rapidly delineating the sunflower subfield areas were used. The crop achene yields in the whole field ranged from 1.3 to 3.77 t ha-1 with a significant within-field spatial variability. The cluster analysis of hand-sampled data showed three subfields with achene yield mean values of 3.54 t ha-1 (cluster 1), 2.98 t ha-1 (cluster 2), and 2.07 t ha-1 (Cluster 3). In the cluster analysis of NDVI data, the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability. The best period for identifying subfield areas starts from the inflorescences development stage to the development of the fruit stage. Analyzing the NDVI subfield areas and yield data, it was found that cluster 1 covers an area of 42.4% of the total surface and 50% of the total achene yield; cluster 2 covers 35% of both surface and yield. Instead, the surface of cluster 3 covers 22.2% of the total surface with 15% of achene yield. K-means algorithm derived from Sentinel-2 NDVI images delineates the sunflower subfield areas. Sentinel-2 images and k-means algorithms can improve an efficient assessment of subfield areas in sunflower crops. Identifying subfield areas can lead to site-specific long-term agronomic actions for improving the sustainable intensification of agriculture in the organic farming system.

Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R² of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.

Effect of Pseudomonas putida-producing pyoverdine on copper uptake by Helianthus annuus cultivated on vineyard soils

Bioaugmentation-assisted phytoextraction was used to reduce the Cu load in vineyard soils. While performance is usually the endpoint of such studies, here we identified some mechanisms underlying Cu soil to plant transfer, particularly the role of siderophores in the extraction of Cu from the soil-bearing phases and its phytoavailability. Carbonated vs. non‑carbonated vineyard soils were cultivated with sunflower in rhizoboxes bioaugmented with Pseudomonas putida. gfp-Tagged P. putida was monitored in the soil and pyoverdine (Pvd), Cu, Fe, Mn, and Zn were measured in the soil solution. Trace elements (TE) were analysed in the roots and shoots. Plant growth and nutritional status were also measured. With bioaugmentation, the concentration of total Cu (vs. Cu²⁺) in the soil solution increased (decreased) by a factor of 1.6 to 2.6 (7 to 13) depending on the soil. The almost 1:1 relationship between the excess of Fe + Cu mobilized from the solid phase and the amount of Pvd in the soil solution in bioaugmented treatments suggests that Pvd mobilized Fe and Cu mainly by ligand-controlled dissolution via a 1:1 metal-Pvd complex. Bioaugmentation increased the Cu concentration by 17% in the shoots and by 93% in the roots, and by 30% to 60% the sunflower shoot biomass leading to an increase in the amount of Cu phytoextracted by up to 87%. The amount of Fe, Mn, Zn, and P also increased in the roots and shoots. Contrary to what was expected, carbonated soil did not increase the mobilization of TE. Our results showed that bioaugmentation increased phytoextraction, and its performance can be further improved by promoting the dissociation of Pvd-Cu complex in the solution at the soil-root interface.

Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

Evaluation of the soil profile quality of subsided land in a coal mining area backfilled with river sediment based on monitoring wheat growth biomass with UAV systems

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30 cm topsoil and 90 cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30 cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3 m topsoil + 0.2 m subsoil + 0.2 m sediment + 0.2 m subsoil + 0.3 m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency.

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R ²) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R ² up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

Rapid monitoring of reclaimed farmland effects in coal mining subsidence area using a multi-spectral UAV platform

In eastern China, coal mining has damaged a large amount of farmland, posing a great threat to food security. Backfilling with coal waste, fly ash, and sediments from rivers is an effective method to restore farmland. This study was conducted at the reclaimed area (RA) and the undisturbed area (UA) in Shandong Province, China. Soil and plant analyzer development (SPAD) of corn was selected as an indicator of crop growth. Multi-spectral data was obtained by the unmanned aerial vehicle equipped with a camera. By analyzing the correlation between SPAD and spectral bands, the common vegetation index is improved. Different regression methods were used to construct the SPAD inversion model. The distribution of corn SPAD was monitored to objectively evaluate reclamation technology. The results are as follows: (1) the vegetation index improved using the red-edge band has a higher correlation with SPAD, and the largest coefficient of determination (R²) value is 0.779; (2) the optimum inversion model for both jointing stage (R² = 0.676) and milky stage (R² = 0.661) is the linear regression model; the optimum model for both tasseling stage (R² = 0.809) and filling stage (R² = 0.830) is the partial least squares regression model; (3) the SPAD inversion map of RA and UA obtained by the optimum model shows that the corn grown in RA is slightly better than in UA. This study realized the rapid and efficient monitoring of the reclamation effects based on multi-spectral imagery and verified the feasibility of backfilling reclamation with Yellow River sediment in coal mining subsidence areas.

High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor

INTRODUCTION

In recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season.

MATERIALS AND METHODS

We therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well.

RESULTS AND DISCUSSION

A linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R²: VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.