Search Results (26)

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

Oat (Avena sativa L.) is one of the most important self-fertilizing field plants belonging to the Poaceae family. It has no significant requirements regarding growing conditions but has a very good reaction to fertilization. The current research evaluated the significance of the effects of individual applications of mineral (NPK) and organo-mineral (OMF) fertilizers, as well as their individual combination with slaked lime (calcium hydroxide, Ca(OH)₂), on the yield, morphological traits [mean number of leaves per plant—MNLP, minimum leaf length (cm) per plant—MinLL, maximum leaf length (cm) per plant—MaxLL, number of ears per plant—NEP], and physiological parameters (nitrogen balance index—NBI, content of chlorophyll—Chl, flavonoids—Flv, anthocyanins—Ant) of organic and pelleted (graded) oat plants, comparing the treatments and in relation to the control. The experiment was performed in semi-controlled glasshouse conditions, in pots, from the fourth week of March to the fourth week of June 2024, using Vertisol soil. This soil is characterized as light clay with an acid reaction. Physiological parameters were measured using a Dualex leaf clip sensor. The results obtained showed that physiological parameters in both oat types significantly differed (p < 0.05) between the treatments applied and in relation to the control, whereas the morphological traits did not significantly differ (p > 0.05) between the treatments. Statistically significant differences (p < 0.05) in the yield of both oat types were most pronounced in the OMF + Slaked Lime treatment (organic: 4.49 g pot⁻¹; pelleted: 4.61 g pot⁻¹) in relation to the control (organic: 2.48 g pot⁻¹; pelleted: 2.63 g pot⁻¹). The pelleted oats showed slightly better results for the effects of different treatments across all tested parameters compared to organic oats. In conclusion, the best results were obtained with the use of OMF + Slaked Lime, which could be proposed as the optimal fertilization treatment for pelleted and organic oat cultivation based on this research. Full article

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models. Full article

A number of studies have indicated the potential role of secondary metabolites, referred to as ‘resistance factors’, in plant defence against insect pests. Nevertheless, it remains unclear which metabolites serve as predictors of resistance in chrysanthemum cultivars against thrips. In the present study, the phenolic compounds of chrysanthemum leaves at different ontogenetic stages were analysed using high-performance liquid chromatography (HPLC). Furthermore, the relative epidermal flavonol contents in the leaves were quantified using the Dualex^® Scientific 4 sensor, and the suitability of this non-destructive method for the rapid discrimination of resistance levels was evaluated. The results demonstrated that the most notable discrepancies in phenolic metabolite profiles were observed in the older leaves and the vegetative state of the chrysanthemum plants. Multiple discriminant analysis was conducted using HPLC-analysed metabolites to predict the importance of metabolites in resistant, susceptible, or highly susceptible plants in the vegetative stage. The results demonstrated that multiple metabolites, rather than a single metabolite, are responsible for thrips resistance in chrysanthemum. However, the relative flavonol content did not reflect the HPLC-analysed flavonoid glycosides or hydroxycinnamic acid derivatives, indicating that the Dualex^® sensor is not a suitable device for determining resistance levels in chrysanthemums. Testing is required to extend and analyse the results in greater depth. Full article

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R²): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R²: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R²: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas. Full article

The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R² = 0.61–0.7 and R² = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R² = 0.25–0.35 and R² = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties. Full article

Knowledge about the spectral response of camelina under different regimes of nitrogen (N) fertilization is very scarce. Therefore, 2-year open-field trials were carried out in the 2021 and 2022 growing seasons with the aim of evaluating the spectral response of spring camelina to four different N fertilization regimes by using remote (UAV) and proximal (leaf-clip Dualex) sensing techniques. The tested treatments were: (i) control: no N application (T0); (ii) top dressing: 60 kg N ha⁻¹ before stem elongation (T1); basal dressing: 60 kg N ha⁻¹ at sowing (T2); basal + top dressing combination: 60 kg N ha⁻¹ at sowing + 60 kg N ha⁻¹ before stem elongation (T3). Camelina seed yield and N use efficiency were strongly affected by fertilization regimes, with the best results obtained at T2. A reduction in plant development and seed yield was detected in 2022, probably due to the rise in air temperatures. A significant effect of both growing season and N fertilization was observed on the photosynthetic pigments content with the T1 highest values in 2022. The highest seed oil content was achieved at T1, while the protein content increased with increasing N, with the best values at T3. Positive and significant correlations were observed among several vegetation indices obtained through UAV flights (NDVI, MRS705, FGCC) and seed yield, as well as between FGCC and leaf N concentration. Overall, these findings demonstrate the feasibility of utilizing remote sensing techniques from UAVs for predicting seed yield in camelina. Full article

Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. The aim of this study is to compare the RGB and multispectral data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant, and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected was divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Third, we assessed plant level water stress, as a consequence of the root damage by nematodes, using stomatal conductance measured with a porometer and indirectly using plant temperature with an infrared thermometer, and also the stable carbon isotope composition of leaf dry matter.. It was found that the interaction between treatments and crops (ANOVA) was statistically different for only four of seventeen parameters: flavonoid (p < 0.05), NBI (p < 0.05), NDVI (p < 0.05) and the RGB CSI (Crop Senescence Index) (p < 0.05). Concerning the effect of treatments across all crops, differences existed only in two parameters, which were flavonoid (p < 0.05) and CSI (p < 0.001). Grafted plants contained fewer flavonoids ($\overline{\mathrm{x}}$ = 1.37) and showed lower CSI ($\overline{\mathrm{x}}$ = 11.65) than non-grafted plants ($\overline{\mathrm{x}}$ = 1.98 and $\overline{\mathrm{x}}$ = 17.28, respectively, p < 0.05 and p < 0.05) when combining all five years and four crops. We conclude that the grafted plants were less stressed and more protected against nematode attack. Leaf flavonoids content and the CSI index were robust indicators of root-knot nematode impacts across multiple crop types. Full article

Black Locust/Robinia can play an important role in land reclamation due to its pronounced nitrogen fixation capability, fast initial growth and relative high drought tolerance. Hence, we set up a trial to test 12 Black Locust clones and three provenances growing on sandy overburden material within the open cast lignite mine Welzow-Süd (South Brandenburg) in March 2014. Since then, biomass growth of the Black Locust trees was examined and physiological performance was studied on several occasions using chlorophyll a fluorescence and Dualex^® measuring technique. Plant physiological measurements revealed differences in photosynthetic vitality (PI_ABS), although the PI_ABS values followed a similar pattern and sequences across the plot. While the genotypes Fra3 and Roy show the highest photosynthetic vitality, the clones Rog and Rob display the lowest PI_ABS mean values. Chlorophyll and phenol content as well as the nutrition supply of the test trees vary depending on their origin and site conditions. The annual biomass growth rate corresponds to photosynthetic vitality and both depend on weather conditions during the growing season. After six years, the growing biomass amounts to 14.7 Mg d.m. ha⁻¹ for clone Rob and 44.8 Mg d.m. ha⁻¹ for clone Fra3, i.e., 2.5 to 7.5 Mg d.m. ha⁻¹ year⁻¹. Our data demonstrate a good correlation between biophysical parameters and biomass growth. We, thus, infer that physiological measuring methods can be combined to strengthen predictions regarding the physiological performance of Black Locust origins. Full article

The “soilless” cultivation of blueberry (Vaccinium corymbosum L.) in containers with peat as substrate allows overcoming the problem of unsuitable soils, thus enhancing the spread of this crop in new areas. Since the use of peat presents several critical environmental and economic sustainability issues, the evaluation of alternative solutions is required. The effectiveness of compost produced with solid digestate and residues from the vine-wine chain to replace part of the peat was therefore tested. Micropropagated plants of cultivar Duke grown in three substrates consisting of a mixture of commercial peat with three compost fractions (10, 20, 40%) were compared with plants grown in 100% unfertilized or fertilized peat (0.3 g of Osmocote per pot). Plant height did not significantly differ between the five theses at the end of the trial, whereas the total number of nodes per plant was higher than in the control theses, due to a greater development of secondary shoots. The nutritional status of the plants, monitored with Dualex, during the growing season, was generally not significantly different in the innovative substrates compared to peat alone. In mid-summer the plants grown in substrates with compost showed the best nitrogen balance index (NBI values). Plants cultivated with medium-high percentages of compost (20–40%) showed a lower degree of defoliation at the end of the trial, dependent on a slower decline of vegetative activity. The final destructive measures of fresh and dry weight of biomass and of its partitioning between roots and shoots highlight that the use of compost did not negatively affect the production of biomass, but rather, in the theses with the highest percentages of compost (20–40%), root development was stimulated. Full article

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods. Full article

Due to the environmental issues that conventional fertilization is causing, biostimulants are proposed as environmentally friendly alternative for crop nutrition in agriculture. The aim of this study was to determine the effects of new Micro Carbon Technology (MCT^®) fertilizers with biostimulant activity based on humic acids biologically digested from leonardite on pepper plant growth in three different soils with different textures. The assays were performed under controlled conditions in a growth chamber and in commercial greenhouses in Spain. The effects on soil were analyzed after the addition of the fertilizers by microbial respiration and enzymatic activities (hydrolase, dehydrogenase and urease). For the plant assays, biometric parameters (fresh weight and fruit hardness) and foliar analysis (chlorophyll indices and nutrients) were evaluated. Under controlled conditions, the use of these biostimulants resulted in a greater soil microbial activity in a 24 h interval with increased soil enzymatic activity. In plants, a positive correlation was found between fertilizers with biostimulant activity and Dualex indices of leaves and content of macronutrients Ca and Mg. In commercial greenhouses, the fertilizers with biostimulant activity strongly depended on the soil texture. In conclusion, these products have real potential to replace conventional fertilizers in commercial production fields. Full article

Flavonoids are favored compounds in plant responses to UV exposure and act in UV absorption and antioxidant activity. Here, it was investigated, with okra as a model species, how fast plants can react to changing UV conditions and to what extent these reactions take place. Okra (Abelmoschus esculentus) plants were exposed to either full or nearly no UV radiation. The diurnal rhythm of the plants was driven by the UV radiation and showed up to a 50% increase of the flavonoid content (measured optically in the +UV plants). This was reflected only in the trends in UV-absorption and antioxidant activity of the extracts but not in the soluble flavonoid glycosides and hydroxycinnamic acid derivatives. In a second experiment, a transfer from a −UV to a +UV condition at 9:00 CDT showed the immediate start of the diurnal rhythm, while this did not occur if the transfer occurred later in the day; these plants only started a diurnal rhythm the following day. After an adaptation period of seven days, clear differences between the +UV and -UV plants could be found in all parameters, whereas plants transferred to the opposite UV condition settle between the +UV and -UV plants in all parameters. Broadly, it can be seen that the flavonoid contents and associated functions in the plant are subject to considerable changes within one day and within several days due to the UV conditions and that this can have a considerable impact on the quality of plant foods. Full article

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha⁻¹) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status. Full article

Proximal remote sensing devices are novel tools that enable the study of plant health status through the measurement of specific characteristics, including the color or spectrum of light reflected or transmitted by the leaves or the canopy. Among these, RGB images can provide spatially detailed information about crop status including estimates of biomass, chlorophyll (and chlorosis) and fractional vegetation cover. The aim of this study is to compare the RGB data collected during five years (2016–2020) of four fruiting vegetables (melon, tomato, eggplant and peppers) with trial treatments of non-grafted and grafted onto resistant rootstocks cultivated in a Meloidogyne incognita (a root-knot nematode, RKN) infested soil in a greenhouse. The proximal remote sensing of plant health status data collected were divided into three levels. Firstly, leaf level pigments were measured using two different handheld sensors (SPAD and Dualex). Secondly, canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the Normalized Difference Vegetation Index (NDVI) measured with a portable spectroradiometer (Greenseeker). Thirdly, we assessed plant level water stress, as a consequence of the root damage by nematodes, directly using stomatal conductance measured with a porometer, and indirectly using plant temperature with an infrared thermometer and also the stable carbon and nitrogen isotope composition of leaf dry matter. Among the measured parameters, carbon and nitrogen percentage exhibited the highest positive correlation (r = 0.90), whereas flavonoids and NBI (Nitrogen Balance Index) showed the highest inverse correlation (r = −0.87). It was found that the interaction between treatments and crops (ANOVA) was statistically different for only 4 of 17 parameters (flavonoid (p = 0.002), NBI (p = 0.044), NDVI (p = 0.004) and CSI (RGB-based Crop Senescence Index) (p = 0.002). Concerning the effect of treatments across all crops, differences existed only in two parameters, which were flavonoids (p = 0.003) and CSI (p = 0.001). Grafted plants contained less flavonoids ($\bar{\mathrm{x}}$ = 1.37) and showed lower CSI ($\bar{\mathrm{x}}$ = 11.65) than non-grafted plants ($\bar{\mathrm{x}}$ = 1.98 and $\bar{\mathrm{x}}$ = 17.28, respectively, p = 0.020 and p = 0.029) when combining all five years and four crops. We conclude that the grafted plants were less stressed and more protected against nematode attack. Leaf flavonoids and the RGB indexes (CSI) were robust indicators of root-knot nematode impacts across multiple crop types. Full article