Crop Growth Monitoring System Research Articles

Retracted: Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

Retracted: Crop Growth Monitoring System Based on Agricultural Internet of Things Technology

Intelligent crop growth monitoring system using IoT and machine learning

Agriculture is the important part of any developing country. As population increases the food requirement also be increases so the traditional farming not sufficient to fulfill the requirement of food. The development of industries, decreases in land space, insufficient rainfall, chemicals used in farming and peoples are moving towards the urban cities are the challenges of the agricultural field. So we have to use another source of food which we called as hydroponics. In hydroponics we can developed our plant by supplying the nutrients through water without use of soil. In this study we had used the applicability of machine learning algorithm like support vector regression(SVR), Linear Regression, Lasso Regression, Decision Tree(DT), Ridge regression and Random forest to find the crop growth accuracy from the result we found that random forest has given the highest accuracy 95% and the performance are evaluated on various model like Decision Tree Regression having better performance on (R2=0.86), Support Vector Regression having better performance on (MAE=12.65 and RMSE= 21.31) and Lasso Regression having better performance on (MSE=4.51). The whole dataset are stored on csv file and divided into two subpart 80% data is used for training and 20% is used for testing.

Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables

Given the present climate change context, accurate and timely coffee yield prediction is critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying this approach to this specific crop, which is the main contribution of this study. The findings of this research reveal that the elaboration of multiple linear regression models based on a combination of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR) corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9 to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional scale by using only satellite-derived vegetation biophysical variables. This study findings are likely to aid local governments and decision makers in precisely forecasting coffee production early and promptly, as well as in recommending relevant local agricultural policies.

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.

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.

Variable-Rate Fertigation Control for Hydroponics Using an On-The-Go Transpiration Monitoring System

HighlightsAn on-the-go monitoring system was developed for assessing spatial and temporal variations in crop growth.The system was used to realize real-time transpiration estimation for lettuce growth.A variable-rate fertigation system was developed based on on-the-go transpiration monitoring.The variable-rate fertigation system was validated in controlled-environment cultivation of hydroponic lettuce.Abstract. Variable-rate fertigation, as a key component of hydroponic cultivation, ensures the sustainable use of water. However, this fertigation method is challenging to implement because of the difficulty associated with assessing the water needs of plants, which vary with plant growth stage and environmental conditions. In this study, an on-the-go crop growth monitoring system was developed to measure the canopy cover of lettuce plants on a daily basis. The canopy cover during the vegetative growth period could be estimated with an accuracy of 98.5% ±1.7% compared to manual segmentation. A modified Penman-Monteith equation was used to predict the transpiration rate of the lettuce plants. In the first cultivation test, the transpiration rate model exhibited a highly linear relationship with a slope of 0.91, coefficient of determination of >0.9, and standard error of regression of <0.51, compared to the directly measured transpiration rates. In the second cultivation test, an automated system that could variably control the fertigation volume was incorporated with the on-the-go system for monitoring the lettuce transpiration in real time. The proposed system could realize on-the-go mapping of the spatial distribution of the canopy cover of the growing lettuce plants, and the variable fertigation responded to the daily water consumption of the plants with an error of only 7.3%. Moreover, the stable canopy cover growth indicated the higher efficiency of variable fertigation compared to timer-based fertigation systems. The proposed variable-rate fertigation system is a promising and practical approach to implement hydroponics with reduced water and energy consumption. Keywords: Canopy cover, Hydroponic, On-the-go monitoring, Transpiration, Variable fertigation.

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

Mobile mecavision: automatic plant monitoring system as a precision agriculture solution in plant factories

Precision agriculture (PA) is an approach in farming systems based on information and technology to enhance agricultural production. PA is applied in plant factories to produce high-quality vegetables in a controlled environment by implementing an automatic crop growth monitoring system. Optimization of the observation process carried out with computer system techniques used in the monitoring process and assessment of the plant condition. Individual observation of cultivated plants requires a monitoring system and numerous equipment. Therefore, a monitoring system equipped with a mobile mechanism is needed to support the monitoring process of crops individually by a single observer. The objective of this study was to design and implement an automatic plant monitoring system based on mobile mechanisms and computer vision using simple equipment in plant factories. The Mobile Mecavision, a monitoring system consists of a mobility system and an image acquisition system controlled by Raspberry Pi. The components are A4988, NEMA 17, and Logitech C270 HD Webcam. The evaluation for system performance carried out using the extreme point shift system and t-test. The aspect of image functionality evaluated using the Structural Similarity Measure test and the result was obtained an image similarity value of 89% to 92% with a 90% level of confidence. The result of the performance test on the mobility aspect obtained that the system shifted numerically but not statistically. The overall performance of the system indicated that the monitoring system could be applied to monitor the crop growth in plant factories.

Implementation of crop growth monitoring system based on depth perception using stereo camera in plant factory

The plant factory is extensive cultivation to produce a high quality of vegetables under a controllable environment. The concept of Precision Agriculture (PA) was introduced to improve the plant factory production by the implementation of a crop growth monitoring system. Crop growth can be estimated by monitoring of crop height and canopy foliage by the use of computer vision technology. In our previous study, we have introduced a plant height monitoring system based on depth perception using a stereo camera. However, the validity of the various type of leave is necessary to be tested. The objective of this study was to implement the crop growth monitoring system to monitor plant development with various type of leave for system validation and evaluation. The crop growth monitoring system composed of a stereo camera implementing the depth perception for estimating the distance from camera to highest point in the crop. The implementation of the system with various types of leaves and characteristics has been conducted for (a) Samhon, (b) Lettuce, and (c) Pagoda. The developed crop growth monitoring system could perform the time series estimation of crop height with a maximum error of RMSE 0.875 cm on Pagoda, and MAPE of 5.56% on Lettuce. The system demonstrates better estimation on Samhong with minimum error RMSE of 0.408cm and 2.27 % of MAPE. Overall validation for the estimated height vs actual measurement indicates that the coefficient of determination higher than 0.7 means that it has substantial features for estimating the plant height.

Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies

Assimilating remote sensing data with crop growth model is a promising method to estimate crop yields over a large area. However, the method is always subject to the problems with biases in remote sensing products and assimilation weights in practical applications. In this study, we demonstrated the robustness of a ‘spatial assimilation’ method in dealing with the biases in three different remote sensing leaf area index (LAI) products. We further explored assimilation strategies for determining the assimilation weights when using ‘spatial assimilation’ method. Three different remote sensing LAI products were assimilated with MCWLA-Wheat model in the North China Plain during 2008–2015. The results demonstrated that the ‘spatial assimilation’ method was robust in mitigating the influences of biased LAI values and easily coupled with various LAI products based on different sources and retrieving algorithms. Furthermore, we found that the historical experiences of the optimal assimilation weights were not suitable to directly drive data assimilation in the coming seasons. Thus data-assimilation strategies to estimate crop yields without prior knowledge on the optimal assimilation weights were investigated. Two ensemble-mean-based assimilation strategies were recommended, which could reach 84 ~ 98% of yield estimation accuracy using the optimal assimilation weights. This study provides reliable and promising solutions for yield estimation over a large area using data assimilation without being limited by the biased state variables in remote sensing products and the lack of prior knowledge on the optimal assimilation weights. The ‘spatial assimilation’ method and the proposed ensemble-mean-based assimilation strategies have great potentials for wide applications, laying solid foundations for developing crop growth monitoring and yield forecasting system.

Peculiarities of technological adaptation of the CGMS system for agricultural crops monitoring in Ukraine

The Crop Growth Monitoring System (CGMS) is one of the most advanced systems of monitoring the conditions of crops growth and development and forecasting their yields in agrometeorological practice. The CGMS allows to assess the conditions of growth, development and accumulation of productive biomass of a number of agricultural crops - winter wheat, barley, maize, rice, sunflower, potatoes, soybean etc. For each of the crops the system must be adapted to specific territories taking into account meteorological, phenological, biological information and soil characteristics. The paper discusses the peculiarities of technological adaptation of the CGMS system (Crop Growth Monitoring System) including creation of a meteorological database for the period of 2000-2017 using standard meteorological observations of the Ukrainian Hydrometeorological Center (UkrHMC) network; creation of a soil characteristics database by finding a correspondences of taxonomy of the soil map of Ukraine (scale:1:2500000) to classification of soils of the WRB; creation of a database of phenological characteristics such as TSUMEM (sum of temperatures within the period from sowing to coming-up), TSUM1 (sum of temperatures within the period from coming-up to blossoming) and TSUM2 (sum of temperatures within the period from blossoming to maturity) calculated according to the data obtained from agrometeorological posts and stations of the UkrHMC network for the period of 2000 - 2015 with regard to five main crops (winter wheat, maize, spring barley, soybean and sunflower); creation of a statistical crop capacity database at the regional and district levels. In addition, the paper considers spatial schematization of calculations and aggregation of agricultural crops productivity indicators obtained as a result of the WOFOST biophysical model application. It also outlines the scheme of crop capacity forecasting based on administrative units and the estimation of forecast accuracy for winter wheat crop capacity in administrative districts of Kiev region. The link to the website containing results of operation of the CGMS-Ukraine system is as follows: http:/entln.uhmi.org.ua/case/CGMS.

Improving cereal yield forecasts in Europe – The impact of weather extremes

Abstract The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system.

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System.

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

Near real-time prediction of U.S. corn yields based on time-series MODIS data

Annual variation of U.S. corn production is an important matter of world concern. To assure immediate response to large-scale harvest failure in crop exporting regions, as was the case during the severe U.S. drought in 2012, and to enhance global food security, a practical crop growth monitoring system based on satellite data is required. This study developed a practical method for near real-time prediction of U.S. corn yields using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Wide Dynamic Range Vegetation Index (WDRVI) taken 7days before the corn silking stage. We incorporated two algorithms into the MODIS-based corn yield prediction method; namely, (1) a MODIS-based crop classification algorithm in consideration of differences in emergence dates between corn and soybean, and (2) a simple bias correction algorithm for correcting region-dependent yield prediction errors. The method is able to predict the annual variation of national and state level corn grain yields with high accuracy in early August and detect corn yield reductions and poor-harvest regions due to drought damage, as in 2002 and 2012, on a near real-time basis in advance of those provided by the U.S. Department of Agriculture's National Agricultural Statistics Service. The method also predicted a national-level U.S. corn grain yield for 2013, which was 3.8% lower than the NASS-statistical data.

Spatial Distribution of Calibrated WOFOST Parameters and Their Influence on the Performances of a Regional Yield orecasting System

We investigate in this study (i) a redefinition of crop variety zonations at a spatial scale of 10x10 km, and (ii) the influence of recalibrated crop parameters on regional yield forecasting of winter wheat and grain maize in western Europe. The baseline zonation and initial crop parameter set was derived from the operational European crop growth monitoring system (CGMS) which involves the agrometeorological model WOFOST. Air temperature data from 325 weather stations over the 1992-2007 period were used to define new zonations in a 300 x 300 km test site. Two parameters which influenced mostly the phenological development stages (i.e. TSUM1 and TSUM2, the effective air temperature sums from emergence to anthesis, and from anthesis to maturity, respectively) were chosen and calibrated. The CGMS was finally run based on these new recalibrated parameters and simulated crop status indicators were compared with official statistics over the 2000-2007 period. Our results showed that the days of anthesis and maturity were simulated with coefficients of determination (R2) ranging from 0.22 to 0.87 for both crops over the study site. A qualitative assessment of maximum leaf area index and harvest index also revealed a more consistent spatial pattern than the initial zonation in the simulation results. Finally, recalibrated TSUM1 and TSUM2 led to improved relationships between official yield and simulated crop indicators (significant R2 in 17 out of 28 and in 14 out of 59 NUTS3 regions with respect to the best predictor for grain maize and winter wheat, respectively).

Validation of ELPIS 1980-2010 baseline scenarios using the observed European Climate Assessment data set

Local-scale daily climate scenarios are required for assessment of climate change impacts. ELPIS is a repository of local-scale climate scenarios for Europe, which are based on the LARS-WG weather generator and future projections from 2 multi-model ensembles, CMIP3 and EU-ENSEMBLES. In ELPIS, the site parameters for the 1980�2010 baseline scenarios were esti- mated by LARS-WG using daily weather from the European Crop Growth Monitoring System (CGMS) used in many European agricultural assessment studies. The objective of this paper was to compare ELPIS baseline scenarios with observed daily weather obtained independently from the European Climate Assessment (ECA) data set. Several statistical tests were used to com - pare distributions of climatic variables derived from ECA-observed daily weather and ELPIS- generated baseline scenarios. About 30% of selected sites have a difference in altitude of >50 m compared with the CGMS grid-cell altitude that was selected to represent agricultural land within a grid-cell. Differences in altitude can explain significant Kolmogorov-Smirnov test (KS-test) results for distribution of daily temperature and in t-tests for temperature monthly means, because of the well-known negative correlation between temperature and elevation. For daily precipita- tion, the KS-test showed little difference between generated and observed data; however, the more sensitive t-test showed significant results for the sites where altitude differences were large. Approximately 11% of sites showed small positive or negative bias in monthly solar radiation, although 86% sites showed >3 significant t-test results for monthly means. These results can be explained by differences in conversion of sunshine hours to solar radiation used in CGMS and LARS-WG. We conclude that, considering the limitations above, ELPIS baseline scenarios are suitable for agricultural impact assessments in Europe.

Spatio-temporal variation of wheat and silage maize water requirement using CGMS model

The Crop Growth Monitoring System (CGMS) has been applied for spatial biophysical resource analysis of Borkhar & Meymeh district in Esfahan province, Iran. The potentially suitable area for agriculture in the district has been divided into 128 homogeneous land units in terms of soil (physical characteristics), weather and administrative unit. Crop parameters required in the WOFOST simulation model for winter wheat and silage maize, have been calibrated based on experimental data from the study area. The study area has been classified into three cropping calendar zones based on average annual temperature, altitude and latitude. For each zone, a sowing date has been defined for each crop as the starting point of crop growth simulation. Growth of these crops has been simulated for the potential situation in each land unit for 20 years of historical daily weather data. Daily potential evapotranspiration and irrigation requirements of each crop per land unit have been calculated in a postsimulation, on the basis of model outputs. Outputs of the model are crop yield (marketable yield and total biomass) and irrigation requirements per decade. Spatial and temporal variation in irrigation requirements has been analyzed. The temporal variation in crop water requirements is larger than the spati3al variation.

A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union

Wheat is Europe's dominant crop in terms of land use in the European Union (EU25). Most of this wheat area is sown in autumn, i.e., winter wheat in all EU25 countries, apart from southern Italy, southern Spain and most of Portugal, where spring wheat varieties are sown in late autumn. We evaluated the strengths and limitations of a regional implementation of the crop growth model WOFOST implemented in the Crop Growth Monitoring System (CGMS) for calculating yield gaps of autumn-sown wheat across the EU25. Normally, CGMS is used to assess growing conditions and to calculate timely and quantitative yield forecasts for the main crops in Europe. Plausibility of growth simulations by CGMS in terms of leaf area, total biomass and harvest index were evaluated and simulated yields were compared with those from other global studies. This study shows that water-limited autumn-sown wheat yields, being the most relevant benchmark for the largely rain fed wheat cultivation in Europe, are plausible for most parts of the EU25 and can be used to calculate yield gaps with some precision. In parts of southern Europe unrealistic simulated harvest index, maximum leaf area index and biomass values were found which are mainly caused by wrong values of phenology related crop parameters. Furthermore CGMS slightly underestimates potential and water-limited yields, which calls for a calibration using new field experiments with recent cultivars. Estimated yield gap is between 2 and 4tha−1 in main parts of the EU25, is smaller north-western Europe and highest in Portugal.

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover. With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode. ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commission's Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed. Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.