Crop Growth Monitoring Research Articles

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simulating crop growth. In this study, an innovative hybrid approach for estimating the leaf area index (LAI) of paddy rice using climate data was developed using ML and DNN regression methodologies. First, we investigated suitable ML regressors to explore the LAI estimation of rice based on the relationship between the LAI and three climate factors in two administrative rice-growing regions of South Korea. We found that of the 10 ML regressors explored, the random forest regressor was the most effective LAI estimator, and it even outperformed the DNN regressor, with model efficiencies of 0.88 in Cheorwon and 0.82 in Paju. In addition, we demonstrated that it would be feasible to simulate the LAI using climate factors based on the integration of the ML and DNN regressors in a process-based crop model. Therefore, we assume that the advancements presented in this study can enhance crop growth and productivity monitoring practices by incorporating a crop model with ML and DNN plans.

Sentinel-1 to NDVI for Agricultural Fields Using Hyperlocal Dynamic Machine Learning Approach

The normalized difference vegetation index (NDVI) is a key parameter in precision agriculture. It has been used globally since the 1970s as a proxy to monitor crop growth and correlates to the crop coefficient (Kc), leaf area index (LAI), crop cover, and more. Yet, it is susceptible to clouds and other atmospheric conditions that might alter the crop’s real NDVI value. Synthetic Aperture Radar (SAR), on the other hand, can penetrate clouds and is hardly affected by atmospheric conditions, but it is sensitive to the physical structure of the crop and therefore does not give a direct indication of the NDVI. Several SAR indices and methods have been suggested to estimate NDVIs via SAR; however, they tend to work for local spatial and temporal conditions and do not work well globally. This is because they are not flexible enough to capture the changing NDVI–SAR relationship throughout the crop-growing season. This study suggests a new method for converting Sentinel-1 to NDVIs for Agricultural Fields (SNAF) by utilizing a hyperlocal machine learning approach. This method generates multiple on-the-fly disposal field- and time-specific models for every available Sentinel-1 image across 2021. Each model learns the field-specific NDVI (from Sentinel-2 and Landsat-8) –SAR (Sentinel-1) relationship based on recent NDVI and SAR time series and consequently estimates the optimal NDVI value from the current SAR image. The SNAF was tested on 548 commercial fields from 18 countries with 28 crop types and, based on 6880 paired NDVI–SAR images, achieved an RMSE, bias, and R2 of 0.06, 0.00, and 0.92, respectively. The outcome of this study aspires to a persistent seamless stream of NDVI values, regardless of the atmospheric conditions, illumination, or local conditions, which can assist in agricultural decision making.

Monitoring Gases Content in Modern Agriculture: A Density Functional Theory Study of the Adsorption Behavior and Sensing Properties of CO2 on MoS2 Doped GeSe Monolayer.

The reasonable allocation and control of CO2 concentration in a greenhouse are very important for the optimal growth of crops. In this study, based on density functional theory (DFT), an MoS2–GeSe monolayer was proposed to unravel the issues of the lower selectivity, poorer sensitivity and non-recyclability of traditional nanomaterial gas sensors. The incorporation of MoS2 units greatly enhanced the sensitivity of the pure GeSe monolayer to CO2 and the high binding energy also demonstrated the thermal stability of the doped structures. The ideal adsorption energy, charge transfer and recovery time ensured that the MoS2–GeSe monolayer had a good adsorption and desorption ability. This paper aimed to solve the matter of recycling sensors within agriculture. This research could provide the theoretical basis for the establishment of a potentially new generation of gas sensors for the monitoring of crop growth.

Developing and evaluating the feasibility of a new spatiotemporal fusion framework to improve remote sensing reflectance and dynamic LAI monitoring

Multi-sensor fusion provides an effective way for applications requiring remote sensing data with high spatiotemporal resolution. Especially for agricultural areas with complex planting structures and rapid changes in crop phenology, more detailed and dense time-series remote sensing data are necessary. The Sentinel-2 Multispectral Imager (S2-MSI) sensor with high spatial resolution (10–60 m) and temporal resolution (5–10 days) plays a key role in spatiotemporal fusion. But the inconsistent spatial resolution of the various bands hinders its potential application at 10 m resolution, and the multiple available fine images it provides are not fully utilized for spatiotemporal fusion. It is worth exploring how to maximize the spatial and temporal resolution of S2-MSI images to help improve the effect of spatiotemporal fusion and the dynamic monitoring of rapid crop growth. In this research, a new spatiotemporal fusion (STF) framework is developed to fuse the S2-MSI image (10 m) enhanced by Super-Resolution for multispectral Multiresolution Estimation (SupReME) algorithm and MODIS image (460 m) with a large spatial ratio (46). The proposed fusion method in the new STF framework combines the existing STF methods with Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm, abbreviated as CA-STF. The accuracy of the fused reflectance and its capability for dynamic LAI monitoring were tested in Daman Superstation of Heihe watershed. The results indicate that: (1) the new STF framework is competent to fuse multi-source images with a ratio of 46 and outperforms the existing STF methods for both near-real-time and post-growth applications; (2) the proposed CA-STF method improves the fusion accuracy and spatial details even if only two S2-MSI images are available, especially for post-growth applications; (3) the vegetation indices (VIs) calculated from the fused images by the new STF framework provide a better correlation with LAINet measurements and improve dynamic LAI monitoring in accuracy and spatial details. This study proposes a framework to maximize the spatial and temporal resolution of S2-MSI images for spatiotemporal fusion. The synthetic daily time-series images with a high resolution of 10 m will have great potential for monitoring the dynamic changes of the land surface.

Aerial Monitorization—A Vector for Ensuring the Agroecosystems Sustainability

This paper is based on the modernization of work processes in agriculture by ensuring the efficient management of land and equipment and the acquisition of inputs given the specific natural variation in environmental conditions. Specifically, the paper highlights research from a dual perspective, descriptive and explanatory, according to the methodology of the case study conducted in the field of the agricultural enterprise SC AgriConsorțium SRL, located in the S–W of Romania, by adopting the spatial technology for the aerial monitorization of agricultural crops and for signalizing, in real time, the changes and vulnerabilities of the agroecosystem in order to function and develop sustainably. The research aims to promote spatial technologies to monitor crop growth resources, crop vegetation conditions, the real-time signaling of changes, and vulnerabilities in the agroecosystem. The research study’s results highlight the role of the aerial monitoring of crops and rapid signaling of changes in the agroecosystem, such as vegetation conditions, plant density, quality of applied work, and the destruction of crops by overgrazing for the rapid and relevant assessment of affected areas and damage. The case study of the paper is a modern, innovative, and sustainable tool for digitizing agricultural enterprises to obtain accurate information on changes in the agroecosystem and to adopt a geographical information system for recording and managing data specific to cultivated areas and their use in providing studies and reports necessary for state institutions, respectively, in order to support and guide the decision-making process. The obtained results are the basis for future research on the interpretation and use of information obtained by drones.

Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion

Drought management largely depends on the availability of timely, accurate and integrated information about its characteristics. Concurrently, biostimulants could represent a sustainable measure to foster the resilience of cropping systems under water-limited conditions. Nevertheless, scientific recognition of the potential of biostimulants has not grown as fast as the interest from industry: therefore, there is an urgent need to investigate biostimulant action. In recent decades, remote sensing has been successfully applied to crop growth and stress monitoring. The use of radiative transfer models, often rooted in artificial intelligence, to estimate plant traits from remotely sensed data can be considered the link between the generalisation and the spatialisation of data, bringing field phenotyping and remote sensing closer together. In this framework, the present study was designed as a factorial combination of irrigation treatment (3 levels) and biostimulant treatment (3 levels) and conducted on processing tomato in open field during the summer of 2020. PROSAIL inversion was carried out to retrieve three major biophysical traits (LAI, LCC, CCC). The parameters dynamics during the season were investigated through GAM modelling. The validation of the model was carried out with a positive outcome in terms of accuracy. The PROSAIL inversion enabled the efficient retrieval of LAI and LCC at rates comparable to those in literature, while performing worse than literature findings only for CCC, probably due to the characteristics of tomato canopy. At the same time, the effect of irrigation was detected both for yield and quality data and detected through the GAM modelisation of the parameters. However, no biostimulant effect could be detected. The internal variability per plot of the retrieved biophysical traits was high. This, jointly with the uncertainty surrounding biostimulant testing and the magnitude of biostimulant effects, corroborated by the absence of results regarding biostimulant effect on yield and DM, lead to hypothesise that the bottleneck was linked to the biostimulant effect itself.

Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

In order to improve the effect of crop growth monitoring, this paper combines the Internet of Things technology to construct a crop growth monitoring system based on the agricultural Internet of Things technology. Moreover, this paper combines the fuzzy theory to process the data of the agricultural Internet of Things to reduce the uncertainty of the data processing of the agricultural Internet of Things. After improving the fuzzy algorithm, this paper builds an intelligent model, builds the system structure framework according to the actual needs of crop growth monitoring, sets up and analyzes the environmental parameters, and builds the crop growth monitoring system from multiple perspectives. Finally, after constructing the system structure of this paper, the performance of the system proposed in this paper is verified. From the test results, it can be seen that the crop growth monitoring system based on the agricultural Internet of Things proposed in this paper has a good effect.

UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages

• LCC was estimated based on the UAV-based image analysis. • The response stability of vegetation index and LCC was analyzed under different coverage. • Different variable screening methods were used to optimize the spectral parameters. • The spatial distribution map of maize LCC was constructed to provide potentials for fertilization application. Efficiently estimating chlorophyll content is important in monitoring the photosynthesis capacity and growth status of maize canopy in precision agriculture management. Vegetation index (VI) easily obtained by proximal remote sensing has been used as a non-destructive and high-throughput way in crop monitoring, especially in chlorophyll estimation. However, the estimated results of the field chlorophyll content by VIs always face challenges from soil background inhibition and estimation stability under the dynamic changes of vegetation biomass. Thus, an unmanned aerial vehicle (UAV)-based chlorophyll content estimation was conducted by evaluating VI responses under different crop coverages. An analysis was conducted on 36 VIs under different crop coverage conditions to explore their response differences and robustness for chlorophyll estimation. This work focused on the three kinds of VIs named simple vegetation index, modified vegetation index, and functional vegetation index. In 2020, at the experimental station of Dryland Farming Institute of Hebei Academy of Agriculture and Forestry Sciences, UAV carrying multispectral sensor was used to collect visible and near-infrared images of the canopy at the jointing stage of maize under six fertilization levels to obtain VIs. After the UAV fled, ground calibration and sample collection were performed simultaneously, and chlorophyll content was measured. For data processing, correlation coefficient method (CCM) and maximal information coefficient (MIC) were first used to analyze the correlation response characteristics of VIs and chlorophyll content under three different coverage levels. The results showed that when the level of canopy coverage was increased, the linear correlation between VIs and chlorophyll content was substantially reduced. The MIC response indicating linear and non-linear combination relationship was more robust. In addition, the VIs obtained by UAV had a significant linear correlation with maize canopy chlorophyll under low (0.05–0.35) and medium (0.35–0.48) coverage, but an obvious non-linear correlation under high (0.48–0.75) coverage. Chlorophyll-sensitive parameters were then screened based on methods of CCM, MIC, and random frog method (RFM), respectively. Partial least squares regression (PLS) and random forest (RF) algorithms were used to establish the maize canopy chlorophyll content detection models. The findings showed that when Green minus red vegetation index (GMR), Red light normalized value (NRI), Normalized difference red edge (NDRE), Modified simple ratio with red edge (MSRREG), Enhanced Vegetation Index (EVI), Normalized red green difference vegetation index (NDIg), Normalized red blue difference vegetation index (NDIb), Soil-adjusted vegetation index (SAVI), Optimized soil-adjusted vegetation index with red edge (OSAVIREG), Soil-atmospherically resistant vegetation index (SARVI) were selected based on RFM as the optimal spectral variables, the chlorophyll content detection model constructed based on PLS had the least numbers of characteristic variables and the best model accuracy. The training set R 2 and RMSE were 0.753 and 2.089 mg/L, respectively, and the verification set R 2 and RMSE were 0.682 and 2.361 mg/L, respectively. Field chlorophyll content and detection error distribution maps were also drawn and combined with the distribution of fertilization management to provide support for the UAV monitoring of crop growth in the field and variable fertilization management decisions.

Crop Growth Monitoring System in Vertical Farms Based on Region-of-Interest Prediction

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly.

A hyperspectral R based leaf area index estimator: model development and implementation using AVIRIS-NG

Hyperspectral Remote Sensing (HRS) data is vital for crop growth monitoring due to availability of contiguous bands. This research work provides a new novel crop estimator model given the name “Crop Stage estimator” developed using the HRS data on an open-source R platform. The generic model structure provides an easy way to test and modify the importance of crop parameter namely Leaf Area Index to deduce crop growth stages of winter wheat (Triticum aestivum L.) particularly during –heading, tillering and booting. Further, to know the LAI variations at different agriculture sites, the best model was implemented using the AVIRIS-NG (Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation) hyperspectral datasets. The analysis indicates that during tillering stage the performance was found best during calibration (r = 0.66, RMSE =0.40, and Bias =-0.80) and validation (r = 0.98, RMSE =0.20, and Bias =0.12) in comparison to the ground measurements.

High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016–2020

Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016–2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer’s and user’s accuracies are 91% ± 7.8% and 89% ± 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R2 as 0.77 ± 0.15 for 2016–2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository.

Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model

Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

Estimating Leaf Area Index and biomass of sugarcane based on Gaussian process regression using Landsat 8 and Sentinel 1A observations

ABSTRACT Accurate estimation of crop parameters, such as Leaf Area Index (LAI) and biomass over large areas using remote sensing techniques, is crucial for monitoring crop growth and yield prediction. In this study, a Gaussian Process Regression (GPR) method was developed to estimate LAI and biomass values of sugarcane during growth season using optical and synthetic-Aperture Radar (SAR) data fusion. Predicting LAI on an independent test data set using the GPR and the combined optical and SAR indices provided better prediction accuracies of LAI; with the GPR based on radial basis function (Root Mean Square Error [RMSE] = 0.34, Mean Absolute Error [MAE] = 0.28 and Mean Absolute Percentage Error [MAPE] = 10.5%) and polynomial function (RMSE = 0.42, MAE = 0.31 and MAPE = 12.58%), respectively. The test results of sugarcane biomass also showed that the GPR (poly) produced the highest statistical results (RMSE = 2.45 kg/m2, MAE = 1.72 kg/m2, MAPE = 8.1%) using the combined indices. The results suggest that the crop biophysical retrieval based on optical and SAR data fusion and GPR proposed in this study could improve LAI and biomass estimation that could help for effective crop growth monitoring and mapping applications.

An IoT Application for Effective Management of Agriculture Resources

Several issues related to traditional agriculture have hindered the growth of nation. Thus, solution to this mode of agriculture is adoption of smart agriculture. Therefore, the aim of this research is to make the agriculture smart by adopting innovative and advanced technologies such as Internet of things (IoT) and Wireless Sensor Network (WSN) enabled automation in agriculture. In recent times, IoT and WSN based technologies can be applied in several research fields. IoT enables various applications employed in crop growth monitoring and selection of irrigation decision support system. The technology like Raspberry Pi used recently in irrigation system of agriculture for improvement of crop productivity. This research paper focuses on various parameters of IoT related to agriculture such as soil moisture sensor, floodwater detector, temperature tracking sensor, humidity and light intensity sensor, etc. The sensors are associated to web server through raspberry pi controller. It detects the each activity of field and transfer the information to web server. The result outcomes assist the former to know about the actual scenario of field in real time through buzzer and can smartly do watering and showering fertilizer to the field in very efficient manner without trying too hard and quality of the crops will be get improved.

Use of biostimulants in millet as strategies for tolerance to salinity of irrigation water

Millet is grass with high forage potential in semi-arid regions, both for its versatility of use and nutritional quality. The objective of this study was to evaluate the influence of a biostimulant and the plant extract (Cyperus rotundus) on the growth forage, and grain production in millet (cultivar IPA BULK 1BF) submitted to salt stress conditions. The research was carried out at the Serra Talhada Academic Unit, Federal Rural University of Pernambuco, in the Semiarid region of the Northeast of Brazil, from February to April 2017. The experiment was installed in randomized blocks, in a 3x4 factorial scheme, composed of a biostimulant (ACADIAN®), nutsedge extract, and the control, in four salinity levels of the irrigation water, electrical conductivities of 0, 1, 2 and 4 dS m-1, with four repetitions. Biometric analysis of all plants was carried out weekly to monitor crop growth. At 77 days after emergence, measures of net CO2 assimilation and transpiration rates were obtained. The harvest occurred with the maturation of the grains (ED9), being analyzed the dry mass of the different morphological components of the plant. The biostimulant at the level of 2 dS m-1 promoted an increase of 66% in the leaf area of millet compared to the control. With 4 dS m-1, the nutsedge extract provided an increase of 253% in the leaf area compared to the control. These expressive results obtained with the use of these compounds reflected in a production of dry leaf blade mass. The IPA BULK 1 BF millet cultivar has tolerance to the salinity levels studied in this research. The nutsedge extract and the biostimulant are alternatives capable of stimulating the growth and the production of forage of millet under the presence of salts in the irrigation water, however, these compounds have no influence on grain production

Developing machine learning models with multi-source environmental data to predict wheat yield in China

Crop yield is controlled by different environmental factors. Multi-source data for site-specific soils, climates, and remotely sensed vegetation indices are essential for yield prediction. Algorithms of data-model fusion for crop growth monitoring and yield prediction are complicated and need to be optimized to deal with model uncertainty. This study integrated multi-source environmental variables (e.g., satellite-based vegetation indices, climate data, and soil properties) into random forest (RF) and support vector machine (SVM) models for wheat yield prediction in China. The performance of both RF and SVM models was investigated using different types of vegetation indices associated with other predictors. Relative importance and partial dependence analyses were used to identify the main predictors and their relationships with wheat yield. We found that using remotely sensed vegetation indices improved our model precision, and that near-infrared reflectance of terrestrial vegetation (NIRv) was slightly better than normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in predicting yield. NIRv was better in detecting climate stress on crops, and could capture more information regarding crop growth and yield formation. Compared with the SVM model, the RF model with NIRv and other covariates had better performance in wheat yield prediction, with R2 and RMSE being 0.74 and 758 kg/ha respectively. We also found that NIRv from jointing to heading was the most important predictor in determining yield, followed by solar radiation (especially during tillering–heading), relative humidity (during planting–tillering), soil organic carbon, and wind speed (throughout the growing season). In addition, wheat yield exhibited threshold-like responses to most factors based on our RF model. These threshold values can help to better understand how different environmental factors limit wheat yield, which will provide useful information for climate-adaptive crop management. Our findings demonstrated the potential of using NIRv for yield prediction. This approach is broadly applicable to other regions globally using publicly available data.

Analysis on Chlorophyll Diagnosis of Wheat Leaves Based on Digital Image Processing and Feature Selection

Crop nutrition measurement is of great significance in agricultural practice, especially in variable rate fertilization. The chlorophyll content, an important indicator of nitrogen nutrition in crops, largely depends on crop growth and development, photosynthesis, and crop yield, and plays an important role in the monitoring of crop growth. This paper tries to detect the chlorophyll content of wheat quickly, using the digital image processing technology. Specifically, a feature selection method was developed based on wrapper and light gradient boosting machine (LGBM), and combined with logistic regression (LR) to predict the chlorophyll content of wheat. The results show that: the optimal model is the combination between the 17 image evaluation indices screened by LGBM and the LR prediction model; the optimal results were coefficient of determination (R2) of 0.728, and root mean square error (RMSE) of 4.979. The optimal model can predict the chlorophyll content of wheat accurately based on digital images in field prototype.

Using Unmanned Aerial Vehicle-Based Multispectral Image Data to Monitor the Growth of Intercropping Crops in Tea Plantation.

Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R2 = 0.811, root-mean-square error (RMSE) = 0.137 kg/m2, normalized RMSE (NRMSE) = 0.134; mung bean: R2 = 0.751, RMSE = 0.078 kg/m2, NRMSE = 0.100) and LAI (red bean: R2 = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R2 = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.

Research on Tea Tree Growth Monitoring Model Using Soil Information.

Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization.