Estimation of leaf nutrition status in degraded vegetation based on field survey and hyperspectral data

Native Arbuscular Mycorrhizal Fungi Promote Plukenetia volubilis Growth and Decrease the Infection Levels of Meloidogyne incognita

The use of arbuscular mycorrhizal fungi (AMF) offers promising benefits to agriculture in the Amazon regions, where soils are characteristically acidic and nutrient-poor. The purpose of this research was to investigate the potential effects of two recently described species of AMF (Nanoglomus plukenetiae and Rhizoglomus variabile) native to the Peruvian Amazon for improving the plant growth of Plukenetia volubilis (inka nut or sacha inchi) and protecting the roots against soil pathogens. Two assays were simultaneously conducted under greenhouse conditions in Peru. The first focused on evaluating the biofertilizer effect of AMF inoculation, while the second examined the bioprotective effect against the root knot nematode, Meloidogyne incognita. Overall, the results showed that AMF inoculation of P. volubilis seedlings positively improved their development, particularly their biomass, height, and the leaf nutrient contents. When seedlings were exposed to M. incognita, plant growth was also noticeably higher for AMF-inoculated plants than those without AMF inoculation. Nematode reproduction was significantly suppressed by the presence of AMF, in particular R. variabile, and especially when inoculated prior to nematode exposure. The dual AMF inoculation did not necessarily lead to improved crop growth but notably improved P and K leaf contents. The findings provide strong justification for the development of products based on AMF as agro-inputs to catalyze nutrient use and uptake and protect crops against pests and diseases, especially those that are locally adapted to local crops and cropping conditions.

The role of remote sensing in tropical grassland nutrient estimation: a review

The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

Estimation Model of Potassium Content in Cotton Leaves Based on Wavelet Decomposition Spectra and Image Combination Features

Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best "CWT spectra" model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of "CWT-9 spectra + texture," and its determination coefficients (R ²val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (R ²val = 0.66, RMSEval = 0.34), the R ²val increased by 0.24. Different from our hypothesis, the combined feature based on "CWT spectra + color + texture" cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.

Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data

Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small.

Estimating Carbon, Nitrogen, and Phosphorus Contents of West–East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.

Plant design gets its details: Modulating plant architecture by phase transitions

Plants evolved different strategies to better adapt to the environmental conditions in which they live: the control of their body architecture and the timing of phase change are two important processes that can improve their fitness. As they age, plants undergo two major phase changes (juvenile to adult and adult to reproductive) that are a response to environmental and endogenous signals. These phase transitions are accompanied by alterations in plant morphology and also by changes in physiology and the behavior of gene regulatory networks. Six main pathways involving environmental and endogenous cues that crosstalk with each other have been described as responsible for the control of plant phase transitions: the photoperiod pathway, the autonomous pathway, the vernalization pathway, the temperature pathway, the GA pathway, and the age pathway. However, studies have revealed that sugar is also involved in phase change and the control of branching behavior. In this review, we discuss recent advances in plant biology concerning the genetic and molecular mechanisms that allow plants to regulate phase transitions in response to the environment. We also propose connections between phase transition and plant architecture control.

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

The distribution of climbing chalk on climbed boulders and its impact on rock-dwelling fern and moss species

Rock climbing is popular, and the number of climbers rises worldwide. Numerous studies on the impact of climbing on rock-dwelling plants have reported negative effects, which were mainly attributed to mechanical disturbances such as trampling and removal of soil and vegetation. However, climbers also use climbing chalk (magnesium carbonate hydroxide) whose potential chemical effects on rock-dwelling species have not been assessed so far. Climbing chalk is expected to alter the pH and nutrient conditions on rocks, which may affect rock-dwelling organisms. We elucidated two fundamental aspects of climbing chalk. (a) Its distribution along nonoverhanging climbing routes was measured on regularly spaced raster points on gneiss boulders used for bouldering (ropeless climbing at low height). These measurements revealed elevated climbing chalk levels even on 65% of sampling points without any visual traces of climbing chalk. (b) The impact of climbing chalk on rock-dwelling plants was assessed with four fern and four moss species in an experimental setup in a climate chamber. The experiment showed significant negative, though varied effects of elevated climbing chalk concentrations on the germination and survival of both ferns and mosses. The study thus suggests that along climbing routes, elevated climbing chalk concentration can occur even were no chalk traces are visible and that climbing chalk can have negative impacts on rock-dwelling organisms.

Evaluation of Cotton Emergence Using UAV-Based Narrow-Band Spectral Imagery with Customized Image Alignment and Stitching Algorithms

Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes efficiently and build panoramas for each narrow band. The normalized difference vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence.