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Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G − B’ and ‘2G − R − B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops.

In recent decades, organic kiwifruit farming has come up as a feasible method for high-quality kiwi production without using chemical fertilizers. The primary objective of this research was to investigate how the sole application of organic and the combined application of organic manures affected the growth, yields, and quality of Allison kiwifruit, as well as the soil’s physicochemical characteristics. The field trial was conducted on cv. Allison to determine the efficacy of organic manures (OM) on growth, nutrient absorption, production and soil health. The experiment involved eight treatments, viz.: T1: 100% Dairy manure (DM); T2: 100% Vermicompost (VC); T3: 100% chicken manure (CM); T4: 50% DM + 50% CM; T5: 50% DM + 50% VC; T6: 50% CM + 50% VC; T7: DM + CM + VC in equal proportions; and T8: Recommended nutrients inorganic NPK + 40 kg DM. A randomized complete block design comprising three replicas was used in this investigation. The use of inorganic fertilizers (NPK) in combination with DM enhanced Spad Values Chlorophyll, fruit production, leaf number, leaf area, and stem diameter while also improving the soil’s chemical characteristics. The flower initiation was recorded with DM and Vermicompost (50:50). Furthermore, when compared to inorganic fertilizer treatment, OM treatment significantly improved fruit quality by improving fruit chemical composition in terms of soluble solids contents and leaf nutrient status, as well as improving soil’s physical properties with DM and Vermicompost (50:50). The study’s outcome revealed that OM had a significant impact on flowering time, fruit SSC, leaf nutritional status, and soil physical characteristics. In comparison to organic treatments, recommended fertilizer dosages (NPK + DM) improved plant growth, fruit yield, and soil chemical characteristics.

Contamination of soils with heavy metals (HMs) caused serious problems because plants tend to absorb HMs from the soil. In view of HM hazards to plants as well as agro-ecosystems, we executed this study to assess metal toxicity to mung bean ( Vigna radiata ) plants cultivated in soil with six treatment levels of cadmium (Cd) and nickel (Ni) and to find metal tolerant variety, i.e., M-93 (V 1 ) and M-1(V 2 ) with multifarious plant biochemical and physiological attributes. Increasing doses of Cd and Ni inhibited plant growth and photosynthesis and both varieties showed highly significant differences in the morpho-physiological attributes. V 2 showed sensitivity to Cd and Ni treatments alone or in combination. Tolerance indices for attributes presented a declined growth of Vigna plants under HM stress accompanied by highly significant suppression in gas exchange characteristics. Of single element applications, the adverse effects on mung bean were more pronounced in Cd treatments. V 1 showed much reduction in photosynthesis attributes except sub-stomatal CO 2 concentration in all treatments compared to V 2 . The yield attributes, i.e., seed yield/plant and 100-seed weight, were progressively reduced in T 5 for both varieties. In combination, we have observed increased mobility of Cd and Ni in both varieties. The results showed that water use efficiency (WUE) generally increased in all the treatments for both varieties compared to control. V 2 exhibited less soluble sugars and free amino acids compared to V 1 in all the treatments. Similarly, we recorded an enhanced total free amino acid contents in both varieties among all the metal treatments against control plants. We conclude that combinatorial treatment proved much lethal for Vigna plants, but V 1 performed better than V 2 in counteracting the adverse effects of Cd and Ni.

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its Rcv2, RMSEcv, and MAEcv are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

The assessment of hull-less barley germplasm for salt tolerance is crucial for future barley breeding programs, as it allows them to be classified for their adaptability to salt stress. Here, the salt tolerance of 224 hull-less barley genotypes was assessed under different NaCl concentrations during the seedling stage. Three hydroponic experiments were conducted, with morpho-physiological characters collected 10, 7 and 10 days after treatment, respectively, for a preliminary, and two subsequent experiments. Results of the first and second experiments revealed that salt-induced the deterioration of various morpho-physiological characters was most pronounced in the number of leaves per plant, shoot heights, root lengths, shoot and root fresh and dry weights. An integrated score (IS) was used to rank the salt tolerance ability and the four highest (X89, X166, X327, and X349; salt-tolerant) and the two lowest ranking accessions (X66 and X386; salt-sensitive) were selected. Principal component analysis (PCA) and hierarchical cluster analysis of the seedling morpho-physiological characteristics among the hull-less barley genotypes showed strong correlations among most characters, which could be used as selection criteria for identifying salt tolerance in the barley germplasm. Furthermore, the combination of IS rank and PCA can be used to identify salt-tolerant and salt-sensitive genotypes of the barley during the seedling stage. These salt-tolerant genotypes hold the potential for developing new barley cultivars with enhanced salt tolerance, and offer opportunities to advance our understanding of the genetic factors involved in barley’s ability to withstand salt stress.

Teak (Tectona grandis) is a valuable tropical timber species, but knowledge about its environmental adaptability for tree breeding is limited. Progeny trial is crucial for improving breeding materials as progeny performance and resilience to environmental stresses can be assessed for future breeding effort. In this study, we examined the variations in growth, crown architecture and leaf traits in a progeny trial in Indonesia. We retained the best components to explain the relationships among 14 traits and analyzed their genotypic correlations. Our results indicate that family differences contribute to the variation in leaf functional traits such as phosphorus content and SPAD value, which indicate chlorophyll content. Further, we identified three major trait axes that explained most of the trait variations by principal component analysis. The first and second axes represented the leaf economics spectrum, and variations related to tree size, respectively, while the third axis represented venation traits. Additionally, leaf chlorophyll content indicated by SPAD value was an effective tool for evaluating progeny performance because of its strong correlations with growth rate and leaf nutrient contents. These findings provide valuable insights for future progeny trial planning in breeding programs toward enhancing resilience and productivity in teak.

In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.

Nitrogen (N) is the most essential element for growth, development, and grain yield determination in crops. However, excessive nitrogen application can result in environmental pollution and greenhouse gas emissions that contribute to climate change. In this study, we used 158 rice genetic resources to evaluate the relationships between the soil and plant analysis development (SPAD) value and grain yield (GY) and its components. The SPAD value ranged between 30.5 and 55.8, with a mean of 41.7 ± 5.3, under normal nitrogen conditions (NN, 9 kg/10a), and between 27.5 and 52.3, with a mean of 38.6 ± 4.8, under low nitrogen conditions (LN, 4.5 kg/10a). Under NN conditions, the SPAD values were in the following order: japonica (43.5 ± 5.8), Tongil -type (41.7 ± 2.5), others (41.7 ± 5.2), and indica (38.3 ± 3.8). By contrast, under LN conditions, the SPAD values were in the following order: Tongil -type (40.4 ± 2.1), others (40.1 ± 4.5), japonica (39.6 ± 5.2), and indica (35.6 ± 3.9). The 158 genetic resources showed no correlation between SPAD and yield. Therefore, the low-decrease rate (LDR) and high-decrease rate (HDR) SPAD groups were selected to reanalyze the relationships between the surveyed traits. The SPAD values were positively correlated with 1000-grain weight (TGW) for both LDR and HDR groups (NN: 0.63, LN: 0.53), However, SPAD and GY were positively correlated only in the LDR group. For TGW, the coefficient of determination ( R 2 ) was 20% and 13% under NN and LN conditions, respectively. For GY, R 2 values of 32% and 52% were observed under NN and LN conditions, respectively. Genetic resources with higher SPAD values in the LDR group exhibited the highest yield (NN: 1.19 kg/m 2 , LN: 1.04 kg/m 2 ) under both NN and LN conditions. In conclusion, we selected 10 genetic resources that exhibited higher GY under both NN and LN conditions with minimal yield reductions. These genetic resources represent valuable breeding materials for nitrogen deficiency adaptation.