Crop Growth Monitoring Research Articles

Discrimination and monitoring of rice cultural types using dense time series of Sentinel-1 SAR data

Details about the spatial extent of rice cultivation in different seasons are essential for ensuring global food security as well as addressing environmental issues and climate change. This study aims to introduce a novel approach to discriminate between different rice cultural types and subtypes and identify their optimum stages to represent crop growth profiles using Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed at 12 days intervals throughout the growth period of summer and kharif rice. Statistical analysis using ANOVA at 5% level of significance was conducted to identify the critical stages of discrimination for all cultural types and subtypes. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during summer and kharif season. A comprehensive analysis revealed significant variations in backscatter profiles among different rice types. The most critical period for effectively distinguishing between various rice cultural types and subtypes was found to be from transplanting to tillering stage. Decision tree algorithm was able to discriminate between rice cultural types and subtypes with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The findings of this study revealed that C-band co and cross-polarized Sentinel-1 data have the potential to detect differences between different cultural types and subtypes of rice and, therefore, will be helpful in crop growth monitoring and agro-ecological change detection in the cultivation pattern.

Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications.

Hyperspectral Estimation of Nitrogen Content in Wheat Based on Fractional Difference and Continuous Wavelet Transform

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the R2 and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation R2 of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation R2 of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring.

Machine Learning Algorithms for the Retrieval of Canopy Chlorophyll Content and Leaf Area Index of Crops Using the PROSAIL-D Model with the Adjusted Average Leaf Angle

The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALAadj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALAadj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALAadj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALAadj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALAadj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALAadj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm−2, ETR = 54.7 μg cm−2, GBRT = 54.9 μg cm−2, STL = 48.3 μg cm−2) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALAadj (namely CCC RMSE: RFR = 93.0 μg cm−2, ETR = 60.1 μg cm−2, GBRT = 60.0 μg cm−2, STL = 68.5 μg cm−2 and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale.

An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery

Accurate crop type mapping is essential for crop growth monitoring and yield estimation. Recently, various machine learning methods have been increasingly used for crop type mapping, but they often lose their validity when directly applied to other regions and years due to differences in the distribution of source and target data, that is, domain shift. To address the problem, we developed a deep adaptation crop classification network (DACCN) based on the idea of unsupervised domain adaptation (UDA). The proposed DACCN mainly consists of two parts, a feature extractor that converts the original input into high-level representations, and a domain aligner where the discrepancy between source and target distributions is measured using multiple kernel variant of maximum mean discrepancy (MK-MMD). Four states in the United States (U.S.) Corn Belt and two provinces in northeastern China were used as study areas, where samples used for model building and accuracy evaluation were collected based on time-series Sentinel-2 imagery and reference maps in 2018 and 2019. Then, three experiments were designed to verify the transferability of DACCN across space, time, and space–time, respectively. In each experiment, the proposed DACCN was compared to deep crop classification network (DCCN), a model with a similar structure to DACCN but without the domain adaptation mechanism, and two machine learning methods, random forest (RF) and support vector machines (SVM). The experimental results showed that DACCN outperformed other models in most transfer cases with overall classification accuracies ranging from 0.835 to 0.922. The DACCN also performed better in spatially continuous mapping with its predicted crop type maps more consistent with the reference ones. As an innovative application of transfer learning in crop type mapping, the methodology proposed in this study effectively addressed the problem of missing labels in target domains and alleviated the negative impact of domain shift.

Paddy Rice Double-Cropping Field Monitoring via Vegetation Indices with Limited Ground Data—A Case Study for Thapanzeik Dam Irrigation District in Myanmar

This study investigated two popular satellite-derived vegetation indices (VIs), MODIS NDVI and EVI, as tools for monitoring crop growth at the Thapanzeik Dam irrigation district in Myanmar, where quality ground data are difficult to obtain. The time-series analysis for seasonal peak VIs presented a considerable improvement in paddy cultivation during 2001–2020 in the irrigation district during summer and monsoon. Fields outside the district had a much lower improvement ratio. Within the irrigation district, a canal with limited water supply was identified via peak and average VIs evaluation. The evaluation of precipitation impact on crop growth demonstrated an opposite impact on crop growth in summer and monsoon cultivations. Water is one of the limiting factors in summer in the irrigation district; thus, precipitation improves water conditions. However, water is not the limiting factor in monsoon; thus, extra water from precipitation, both hydraulically and meteorologically, negatively impacts crop growth. Compared to NDVI, EVI better captured crop growth in densely vegetated surfaces. Meanwhile, information degradation might have occurred with 250 m EVI, using 500 m blue-band reflectance as an input. Thus, the best vegetation index to use depends on the purpose of monitoring and the site situation.

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters.

Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images

Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model’s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model’s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.

Design of Smart Agricultural Monitoring and Management System

One of the important contents of China’s agricultural construction is vegetable planting, and most of the vegetable planting technology is relatively backward. The commonly used greenhouse planting technology is simply using special mechanical planting management methods, which cannot realize real-time monitoring of crop growth. In order to optimize the crop growth environment and improve the yield, a smart agricultural monitoring and management system based on PLC and MCGS configuration technology is designed. The system combines PLC technology, configuration technology and sensor technology to complete the overall structure design of the control system, and separately explains the hardware design, software design and operation and debugging of the system. The experimental results show that the system can remotely monitor and control the growing environment of crops, such as soil moisture, gas temperature and humidity, light intensity and other parameters, and the operation and maintenance are convenient, which provides a reference method for the intelligent development of agriculture.

IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture

Healthy and sufficient crop and food production are very much essential for everyone as the population is increasing globally. The production of crops affects the economy of a country to a great extent. In agriculture, observing the soil, weather, and water availability and, based on these factors, selecting an appropriate crop, finding the availability of seeds, analysing crop demand in the market, and having knowledge of crop cultivation are important. At present, many advancements have been made in recent times, starting from crop selection to crop cutting. Mainly, the roles of the Internet of Things, cloud computing, and machine learning tools help a farmer to analyse and make better decisions in each stage of cultivation. Once suitable crop seeds are chosen, the farmer shall proceed with seeding, monitoring crop growth, disease detection, finding the ripening stage of the crop, and then crop cutting. The main objective is to provide a continuous support system to a farmer so that he can obtain regular inputs about his field and crop. Additionally, he should be able to make proper decisions at each stage of farming. Artificial intelligence, machine learning, the cloud, sensors, and other automated devices shall be included in the decision support system so that it will provide the right information within a short time span. By using the support system, a farmer will be able to take decisive measures without fully depending on the local agriculture offices. We have proposed an IoT-enabled soil nutrient classification and crop recommendation (IoTSNA-CR) model to recommend crops. The model helps to minimise the use of fertilisers in soil so as to maximise productivity. The proposed model consists of phases, such as data collection using IoT sensors from cultivation lands, storing this real-time data into cloud memory services, accessing this cloud data using an Android application, and then pre-processing and periodic analysis of it using different learning techniques. A sensory system was prepared with optimised cost that contains different sensors, such as a soil temperature sensor, a soil moisture sensor, a water level indicator, a pH sensor, a GPS sensor, and a colour sensor, along with an Arduino UNO board. This sensory system allowed us to collect moisture, temperature, water level, soil NPK colour values, date, time, longitude, and latitude. The studies have revealed that the Agrinex NPK soil testing tablets should be applied to a soil sample, and then the soil colour can be sensed using an LDR colour sensor to predict the phosphorus (P), nitrogen (N), and potassium (K) values. These collected data together were stored in Firebase cloud storage media. Then, an Android application was developed to fetch and analyse the data from the Firebase cloud service from time to time by a farmer. In this study, a novel approach was identified via the hybridisation of algorithms. We have developed an algorithm using a multi-class support vector machine with a directed acyclic graph and optimised it using the fruit fly optimisation method (MSVM-DAG-FFO). The highest accuracy rate of this algorithm is 0.973, compared to 0.932 for SVM, 0.922 for SVM kernel, and 0.914 for decision tree. It has been observed that the overall performance of the proposed algorithm in terms of accuracy, recall, precision, and F-Score is high compared to other methods. The IoTSNA-CR device allows the farmer to maintain his field soil information easily in the cloud service using his own mobile with minimum knowledge. Additionally, it reduces the expenditure to balance the soil minerals and increases productivity.

Automation irrigation system using arduino for smart crop field productivity

Agriculture is essential to the prosperity of agricultural countries like India. Thus, the suggested strategy is to use automation and internet of thing (IoT) technology to make agriculture smart. Applications enabled by the IoTs include irrigation decision assistance, crop growth monitoring and selection, and more. an Arduino-powered technology that boosts agricultural productivity. This study's main goal is to find the least quantity of water necessary to grow crops. Most farmers squander a lot of time on the fields rather than concentrating on the water that plants have access to at the right moment. The suggested system determines the required amount of water based on the data obtained from the sensors. Two sensors provide data on the soil's temperature, humidity, amount of sunlight each day, and soil temperature to the base station. The suggested systems must determine the amount of water required for irrigation based on these criteria. The system's main benefit is the use of precision agriculture (PA) in conjunction with cloud computing, which will maximise the use of water fertilisers while maximising crop yields and also assist in determining field weather conditions.

Evaluation of LAI Dynamics by Using Plant Canopy Analyzer and Its Relationship to Yield Variation of Soybean in Farmer Field

Soybean yield largely varies spatially and yearly in farmer fields. Appropriate growth diagnosis is recommended to stabilize the yield. Leaf area index (LAI) is a representative diagnostic item, but an evaluation method of LAI dynamics with growth has not been established. In this study, we utilized a growth function consisting of an exponential function and a power math function. Parameters were derived from the growth function to be analyzed with yield variability. The LAI was measured weekly by a plant canopy analyzer in farmer fields for 4 years. The dynamics were parameterized by fitting the growth function. The relationship between the parameters of LAI dynamics and soybean yield was analyzed. The growth function was well fitted to measured LAI at R2 = 0.82~0.90 and RMSE = 0.54~0.69 m2 m−2. The parameters of the growth function, such as maximum LAI (LAImax) and cumulative temperature at maximum LAI (TLAImax), quantified the spatial and yearly differences in LAI dynamics, partly explaining those in the yield. The growth function utilized in this study is considered a robust method to quantify LAI dynamics and to diagnose soybean production. The quantification of LAI dynamics may help to develop crop growth monitoring with UAVs (Unmanned Aerial Vehicles) remote sensing as a new diagnostic tool.

Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis

In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making.

Remote Sensing from Different Sources for Crop Growth Monitoring in the Area of the Lower Northern Mississippi

Remote sensing monitoring of crop growth began from airborne photography to assist in crop pest management and has evolved into monitoring from satellites, manned aircraft, unmanned aerial vehicles (UAVs), and ground-based systems for crop production process modeling, optimization, and control. In recent years, for crop growth remote sensing monitoring, apart from satellites, manned aircrafts, and ground-based systems, UAVs have been developed and widely used for low-altitude remote sensing of crop fields to uniquely provide a cost-effective, flexible tool for field crop growth remote sensing. Additionally, their data create a critical layer between all other remote sensing platforms. This paper overviews the use of remote sensing from difference sources, especially airborne remote sensing from manned aircraft and UAVs, to monitor crop growth in the area of the lower northern Mississippi from the Mississippi Delta to the Black Prairie, one of the most important agricultural areas in the U.S. In this paper, three sites typical in the area are demonstrated for remote sensing monitoring of crop growth, and the issues and challenges are identified and discussed for future opportunities to integrate remote sensing data from different sources to improve crop monitoring in this area and surrounding areas.

Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network

The prediction of the maturity date of leafy greens in a planting environment is an essential research direction of precision agriculture. Real-time detection of crop growth status and prediction of its maturity for harvesting is of great significance for improving the management of greenhouse crops and improving the quality and efficiency of the greenhouse planting industry. The development of image processing technology provides great help for real-time monitoring of crop growth. However, image processing technology can only obtain the representation information of leafy greens, and it is difficult to describe the causal mechanism of environmental factors affecting crop growth. Therefore, a framework combining an image processing model and a crop growth model based on causal inference was proposed to predict the maturity of leafy greens. In this paper, a deep convolutional neural network was used to classify the growth stages of leafy greens. Then, since some environmental factors have causal effects on the growth rate of leafy greens, the causal effects of various environmental factors on the growth of leafy greens are obtained according to the data recorded by environmental sensors in the greenhouse, and the prediction results of the maturity of leafy greens in the study area are obtained by combining image data. The experiments showed that the root mean square error (RMSE) was 2.49 days, which demonstrated that the method had substantial feasibility in predicting the maturity for harvesting and effectively solved the limitations of poor timeliness of prediction. This model has great application potential in predicting crop maturity in greenhouses.

Estimation of Cotton Nitrogen Content Based on Multi-Angle Hyperspectral Data and Machine Learning Models

For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy’s physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors.

Estimation of Maize Yield and Protein Content under Different Density and N Rate Conditions Based on UAV Multi-Spectral Images

In the field of precision agriculture research, it is very important to monitor crop growth in time so as to effectively conduct field diagnosis and management and accurately predict yield and quality. In this experiment, the relationship between the vegetation index of Zhengdan 958 and Suyu 41 and their yield and quality when reducing N application (25 and 50% N reduction compared to local conventional N application rate) under low, medium and high planting densities (60,000, 75,000 and 90,000 plants·ha−1) during 2018–2020 was investigated using multispectral images obtained from UAV monitoring. The results showed that under different density treatments, the normalized vegetation index (NDVI) and ratio vegetation index (RVI) decreased with the decrease in nitrogen application, while the plant senescence reflectance index (PSRI) increased. Through principal component analysis (PCA) and subordinate function analysis, the comprehensive score of each treatment can reflect the maize yield and total protein content under each treatment. Based on the vegetation index, predictive models of maize yield and protein content were established. The best prediction period for grain yield and protein content were physiological maturity and 35 days after silking (R4), respectively. The R2 of the predictive models are greater than 0.734 and 0.769, respectively. Multi-period and multi-vegetation indexes can better monitor crop growth and help agricultural field management.

Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods

The estimation of physicochemical crop parameters based on spectral indices depend strongly on planting year, cultivar, and growing period. Therefore, the efficient monitoring of crop growth and nitrogen (N) fertilizer treatment requires that we develop a generic spectral index that allows the rapid assessment of the plant nitrogen content (PNC) of crops and that is independent of year, cultivar, and growing period. Thus, to obtain the best indicator for estimating potato PNC, herein, we provide an in-depth comparative analysis of the use of hyperspectral single-band reflectance and two- and three-band spectral indices of arbitrary bands for estimating potato PNC over several years and for different cultivars and growth periods. Potato field trials under different N treatments were conducted over the years 2018 and 2019. An unmanned aerial vehicle hyperspectral remote sensing platform was used to acquire canopy reflectance data at several key potato growth periods, and six spectral transformation techniques and 12 arbitrary band combinations were constructed. From these, optimal single-, two-, and three-dimensional spectral indices were selected. Finally, each optimal spectral index was used to estimate potato PNC under different scenarios and the results were systematically evaluated based on a correlation analysis and univariate linear modeling. The results show that, although the spectral transformation technique strengthens the correlation between spectral information and potato PNC, the PNC estimation model constructed based on single-band reflectance is of limited accuracy and stability. In contrast, the optimal three-band spectral index TBI 5 (530,734,514) performs optimally, with coefficients of determination of 0.67 and 0.65, root mean square errors of 0.39 and 0.39, and normalized root mean square errors of 12.64% and 12.17% for the calibration and validation datasets, respectively. The results thus provide a reference for the rapid and efficient monitoring of PNC in large potato fields.

Estimating vertically growing crop above-ground biomass based on UAV remote sensing

The accurate estimation of crop above-ground biomass (AGB) can assist in crop growth monitoring and grain yield prediction. Remote sensing has been widely used for AGB estimation at regional and local scales in recent years. However, optical remote sensing spectral indices (SIs) become saturated at medium-to-high crop covers. The combined use of remote sensing techniques and statistical regression models is not based on an understanding of how crop leaves and vertical organs contribute to the crop AGB. This causes difficulties in measuring the biomass stored in vertical organs (e.g., plant stem, wheat-spike, maize-tassel; abbreviated as AGBv) using optical remote sensing. This study aims to develop an unmanned aerial vehicle (UAV)-based vertically growing crop AGB (VGC-AGB) model. We defined Csm (g/m) to describe the crop stem and reproductive organs’ average dry mass content. This was done to improve the estimation of AGBv. The crop leaf area index (LAI, m2/m2), leaf dry matter content (Cm, g/m2), height (Ch, m), and density (Cd, m−2) were used in the VGC-AGB. The VGC-AGB calculated crop leaf AGB (AGBl) using LAI × Cm (g/m2) and AGBv using Cd × Ch × Csm (g/m2). The proposed VGC-AGB (AGB = LAI × Cm + Cd × Ch × Csm) was verified using field and UAV-based hyperspectral datasets of winter-wheat and summer-maize at three growth stages. Our results indicate that UAV-based VGC-AGB (R2 = 0.92–0.93, RMSE = 68.82–75.15 g/m2) is superior to the statistical regression model that is based on remote sensing SIs and CSMs (R2 = 0.77, RMSE = 134.94 g/m2). The results indicate that the UAV-based VGC-AGB supports the analysis of crop photosynthetic product transfers and high-performance UAV-based high-performance non-destructive AGB monitoring.