Search Results (80)

Chlorophyll content is a key physiological indicator reflecting photosynthetic capacity, and the Soil–Plant Analysis Development (SPAD) meter is a commonly used tool for its rapid and non-destructive estimation. Hyperspectral imaging (HSI) is a non-destructive technique that captures fine spectral characteristics and thus holds great potential for high-throughput phenotyping and early stress detection. This study aimed to explore the potential of HSI combined with ensemble learning (EL) to estimate SPAD of rapeseed seedlings under different durations of waterlogging. Hyperspectral images and corresponding SPAD values were collected from six rapeseed cultivars at 0, 2, 4 and 6 days of waterlogging. The mutual information was employed to select the top 30 most relevant spectral and vegetation index features. The EL model was constructed using partial least squares, support vector machine, random forest, ridge regression and elastic net as the first-layer learners and a multiple linear regression as the second-layer learner. The results showed that the EL model showed superior stability and higher prediction accuracy compared to single models across various genotypes and waterlogging treatment datasets. As waterlogging duration increased, the overall model accuracy improved; notably, under 6 days of waterlogging, the EL model achieved an R² of 0.79 and an RMSE of 3.27, indicating strong predictive capability. This study demonstrated that combining EL with HSI enables stable and accurate estimation of SPAD values, therefore providing an effective approach for early stress monitoring in crops. Full article

Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly been applied in crop growth monitoring. However, the small, thick, waxy leaves of olive, together with its complex canopy structure and dense arrangement, may reduce estimation accuracy. To identify sensitive features related to olive leaf chlorophyll and to evaluate the feasibility of UAV-based estimation methods for olive trees with complex canopy structures, UAV multispectral orthophotos were acquired, and leaf chlorophyll was measured using a SPAD (Soil Plant Analysis Development) meter to provide ground-truth data. A dataset including single-band reflectance, vegetation indices, and texture features was built, and sensitive variables were identified by Pearson correlation. Modeling was performed with linear regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Partial Least Squares Regression (PLSR), and Support Vector Machine (SVM). Results showed that two spectral bands (green and red), one vegetation index (TCARI/OSAVI), and twelve texture features correlated strongly with SPAD values. Among the machine learning models, XGBoost achieved the highest accuracy, demonstrating the effectiveness of integrating multi-feature UAV data for complex olive canopies. This study demonstrates that combining reflectance, vegetation indices, and texture features within the XGBoost model enables reliable chlorophyll estimation for olive canopies, highlighting the potential of UAV-based multispectral approaches for precision monitoring and providing a foundation for applications in other woody crops with complex canopy structures. Full article

Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with mainstream unmanned aerial vehicle, emerging phenotyping robots can carry multiple sensors and acquire higher-resolution data. Nevertheless, the feasibility of estimating rice SPAD using multi-sensor data obtained by phenotyping robots remains unknown, and whether the integration of machine learning algorithms can improve the accuracy of rice SPAD monitoring also requires investigation. This study utilizes phenotyping robots to acquire multispectral and RGB images of rice across multiple growth stages, while simultaneously collecting SPAD values. Subsequently, four machine learning algorithms—random forest, partial least squares regression, extreme gradient boosting, and boosted regression trees—are employed to construct SPAD monitoring models with different features. The random forest model combining vegetation indices, color indices, and texture features achieved the highest accuracy (R² = 0.83, RMSE = 1.593). In summary, integrating phenotyping robot-derived multi-sensor data with machine learning enables high-precision, efficient, and non-destructive rice SPAD estimation, providing technical and theoretical support for rice phenotyping and precision cultivation. Full article

In this study, we aimed to evaluate physiological and agronomic traits in 120 sugarcane genotypes under early drought stress conditions in a field trial across various soil types. The experiment used a split-plot arrangement, with a randomized complete block design and two replications. Two different water regimes were assigned to the main plot: (1) non-water stress (CT) and (2) drought (DT) at the early growth stage, during which sugarcane was subjected to drought stress by withholding water for 4 months. The subplot consisted of 120 sugarcane genotypes. The stalk height, stalk diameter, number of stalks, photosynthetic traits including SPAD chlorophyll meter reading (SCMR) and maximum quantum efficiency of photosystem II photochemistry (Fv/Fm), and normalized difference vegetation index (NDVI) were measured at 3, 6, and 9 months after planting (MAP). Yield and yield component parameters were measured at 12 MAP. Drought treatments lead to significant changes in various physiological traits in the sugarcane. Clustering analysis classified 36 sugarcane varieties grown in sandy loam soil and 15 genotypes in loam soil into two main clusters. In sandy loam soils, Biotec4 and CO1287 exhibited outstanding performance in drought conditions, delivering high cane yields. Meanwhile, in loam soil, MPT13-118, MPT07-1, Q47, F174, MPT14-1-902, and UT1 exhibited the best drought tolerance. Under drought conditions, cluster 1 showed higher values for SCMR, NDVI, height growth rate (HGR), cane yield, and drought tolerance index compared to cluster 2. These findings suggest that breeders can utilize these genotypes to enhance drought resistance, and the identified physiological traits can assist in selecting stronger candidates for drought tolerance. Full article

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

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

Thrips flavus (Thysanoptera: Thripidae) is a Eurasian pest that primarily attacks a variety of cash crops such as soybean. Currently, there is insufficient knowledge of thrips-resistance mechanisms in soybeans and a lack of effective thrips-resistant soybean varieties. The objective of this study was to identify the correlation between the pest thrips, T. flavus, resistance levels and morphological structures of soybean varieties. A total of 41 spring soybean varieties were planted in a field in Northeast China. Observations were made regarding the infestation intensity of T. flavus, the morphological structures (compound leaf shape, leaf length, leaf width, leaf surface humidity, trichome density, length, and color), leaf SPAD value, leaf nitrogen content, etc. Specifically, leaf trichome density (regardless of whether it was on the upper or lower surfaces of the upper, middle, or lower leaves), trichome color, and compound leaf shape all showed significant positive correlations with the amount of T. flavus. Additionally, principal component analysis (PCA) indicated that, during the peak flowering stage, leaf width, trichome length, trichome density, SPAD value, and nitrogen content were key factors for evaluating resistance; meanwhile, during the podding stage, leaf length, SPAD value, nitrogen content, and leaf surface humidity made the most significant contributions. Field resistance screening using the number of T. flavus per meter of double rows, the average number of T. flavus per plant, and hierarchical cluster analysis yielded consistent results. The soybean variety “podless-trichome” is a thrips-resistant variety (high resistance), and “Jinong 29” is a thrips-sensitive variety (high sensitivity). This study provides valuable insights into the occurrence of insect resistance to thrips in soybean varieties. Full article

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article

Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R²) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R² value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees. Full article

The development of critical levels for sap nitrate and chlorophyll meter reading (SPAD test) in the case of various crops is of great importance for growers in characterizing a plant’s N status. A field experiment with spring oat (Avena sativa L.) was carried out on loamy soil in Debrecen, Hungary, using a small-plot design. Ammonium nitrate was broadcast at rates of 0, 30, 60, and 90 kg N/ha in three replicates. The total N content of the plant, sap nitrate content, and SPAD values were measured at jointing when the first node appeared above the soil surface (Feekes 6) and at boot stage (Feekes 10). Regression analysis between total N content and sap nitrate showed cubic and linear relationships with r² = 0.7982 (Feekes 6, whole plant) and 0.9625 (Feekes 10, upper developed leaves), respectively. Optimal grain yield was obtained when sap nitrate exceeded 650 mg/L and 540 mg/L at Feekes 6 and Feekes 10, respectively. There were linear and logarithmic relationships between total N content and SPAD values with r² = 0.8058 and 0.6258 at Feekes 6 and 10. Optimal grain yield occurred over SPAD values of 43 and 48 at Feekes 6 and 10, respectively. Optimal N rate was 60 kg N/ha on the experimental site. Full article

The present study aimed to investigate the effects of different soil amendments coupled with nitrogen fertilizer on the morpho-physiological characteristics and yield of salt-tolerant rice under saline conditions. The soil amendments, i.e., S1: zeolite amendment, S2: coconut coir amendment, S3: humic acid amendment, and S0: no amendment, and fertilizer treatments, i.e., N1: urea, N2: slow-release urea, and N0: no N fertilizer, were kept in main plots and sub-plots, respectively, in a split-plot design. The salt-tolerant variety ‘Shuangliangyou 138’ was exposed to 0.3% salt irrigation water. The results showed that during the entire growth period, compared to S0, the S1 and S3 treatments increased the SPAD values by an average of 6.3%and 5.5%, respectively, the leaf area index by an average of 24.5% and 19.8%, the canopy interception rate by an average of 11.5% and 4.1%, and the aboveground biomass by an average of 36.8% and 13.9%, respectively. Moreover, under S1 and S3 conditions, the tiller number per square meter, leaf water potential, leaf water content, and chlorophyll contents were also improved under the slow-release urea than urea. Moreover, slow-release urea promoted root vitality and nutrient absorption as well as enhanced the activity of antioxidant and nitrogen metabolism enzymes than urea under the S1 and S3 conditions. In sum, the rational application of soil amendments and slow-release urea could improve the rice productivity on saline-alkali land. Full article

There are few studies on how nitrogen (N) fertilizer application rates and transplanting densities impact rice yield, root distribution, and N use efficiency in the cold regions of Northeast China. This research involved a two-year field trial utilizing Jinongda 667 as the material. In 2021, three N split-fertilizer applications—T1 (6:3:1), T2 (5:3:2), T3 (4:3:3)—and two transplanting densities—D1 (30 cm × 13.3 cm) and D2 (30 cm × 20 cm)—were compared with the conventional cultivation mode (T0: 175 kg N hm⁻², 6:3:1), whereby the N application mode most suitable for increasing density was explored. In 2022, four N application levels—0 (N0), 125 (N1), 150 (N2), and 175 (N3) kg N hm⁻²—were assessed under the same density treatment to analyze the yield, resource utilization efficiency, and root traits of Jinongda 667. The results indicated that when the transplanting density was 30 cm × 13.3 cm, the application of 5:3:2 fertilizer was more conducive to improving rice yield. Increasing planting density under reduced N input significantly enhanced both rice yield and N use efficiency. In contrast to the conventional cultivation method (D2N3), the treatment of increased planting density (D1N2) under reduced N input led to a 21.2% rise in the number of panicles per square meter and an 8.6% boost in rice yield. Furthermore, increasing planting density under reduced N input significantly enhanced the agronomic efficiency of N fertilizer, the apparent utilization rate, and the N harvest index. It also boosted the SPAD value, photosynthetic rate, and the utilization efficiency of light and N resources in rice. However, it was noted that root enzyme activity decreased. This study demonstrated that increasing planting density, combined with the N application mode of 5:3:2 and an N application rate of 150 kg hm⁻², maximized resource utilization efficiency, optimized root absorption capacity, and resulted in higher yields. Full article

Proper crop management requires rapid detection methods for abiotic and biotic stresses to ensure plant health and yield. Hemp (Cannabis sativa L.) is an emerging economically and environmentally sustainable crop capable of yielding high biomass. Nitrogen deficiency significantly reduces hemp plant growth, affecting photosynthetic capacity and ultimately decreasing yield. When symptoms of nitrogen deficiency are visible to humans, there is often already lost yield. A real-time, non-destructive detection method, such as Raman spectroscopy, is therefore critical to identify nitrogen deficiency in living hemp plant tissue for fast, precise crop remediation. A two-part experiment was conducted to investigate portable Raman spectroscopy as a viable hemp nitrogen deficiency detection method and to compare the technique’s predictive ability against a handheld SPAD (chlorophyll index) meter. Raman spectra and SPAD readings were used to train separate nitrogen deficiency discrimination models. Raman scans displayed characteristic spectral markers indicative of nitrogen deficiency corresponding to vibrational modes of carotenoids, essential pigments for photosynthesis. The Raman-based model consistently predicted nitrogen deficiency in hemp prior to the onset of visible stress symptoms across both experiments, while SPAD only differentiated nitrogen deficiency in the second experiment when the stress was more pronounced. Our findings add to the repertoire of plant stresses that hand-held Raman spectroscopy can detect by demonstrating the ability to provide assessments of nitrogen deficiency. This method can be implemented at the point of cultivation, allowing for timely interventions and efficient resource use. Full article

The efficient management of nitrogen (N) on a site-specific basis is critical for the improvement of crop yield and the reduction of environmental impacts. This review examines the application of three primary technologies—canopy reflectance sensors, chlorophyll meters, and leaf color charts—in the context of site-specific N fertilizer management. It delves into the development and effectiveness of these tools in assessing and managing crop N status. Reflectance sensors, which measure the reflection of light at specific wavelengths, provide valuable data on plant N stress and variability. The advent of innovative sensor technology, exemplified by the GreenSeeker, Crop Circle sensors, and Yara N-Sensor, has facilitated real-time monitoring and precise adjustments in fertilizer N application. Chlorophyll meters, including the SPAD meter and the atLeaf meter, quantify chlorophyll content and thereby estimate leaf N levels. This indirect yet effective method of managing N fertilization is based on the principle that the concentration of chlorophyll in leaves is proportional to the N content. These meters have become an indispensable component of precision agriculture due to their accuracy and ease of use. Leaf color charts, while less sophisticated, offer a cost-effective and straightforward approach to visual N assessment, particularly in developing regions. This review synthesizes research on the implementation of these technologies, emphasizing their benefits, constraints, and practical implications. Additionally, it explores integration strategies for combining these tools to enhance N use efficiency and sustainability in agriculture. The review culminates with recommendations for future research and development to further refine the precision and efficacy of N management practices. Full article