An Artificial Intelligence Approach to Manage Crop Water Requirements in South Africa (original) (raw)
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This study compares the daily crop evapotranspiration estimated by artificial neural network (ANN), neural network-genetic algorithm (NNG), and nonlinear regression (NLR) methods. Using a 6-year (2000)(2001)(2003)(2004)(2005) daily meteorological data, recorded at Tabriz synoptic station, and Penman-Monteith FAO 56 standard approach (PMF 56), the daily crop evapotranspiration (ET C ) was determined during the growing season (April-September). Air temperature, wind speed at 2 meters height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of artificial neural network models. In this study, the genetic algorithm was applied for optimization of parameters which used in ANN approach. It was found that the optimization of input parameters did not improve the performance of ANN method. The results indicate that NLR, ANN and NNG methods were able to predict potato crop evapotranspiration (ET C ) at the desirable level of accuracy. However, the NLR method with highest coefficient of determination (R 2 > 0.96, P value<0.05) and minimum errors provided the superior performance among the other methods.
Journal of theoretical and applied information technology, 2013
Cropping pattern is a scheduling for farming time on a certain land in a definite period (e.g. 1 year), including unfilled area. In arranging crop planting patterns, hydrological (rainfall), climatological (temperature, humidity, wind speed, and sunshine), crop (crop coefficient value, productivity and price) and land area data are required. Therefore, a method that can be applied to predict the hydro climatological data is needed. The appropriate method for such prediction is Back Propagation Neural Network (BPNN). Prediction result of BPNN will be used to determine minimum crop water requirements, and it will be associated with planting time (age) of each crop for making cropping pattern. The design of most favorable cropping pattern will obtain the maximum profit and reduce fail harvest problem, which in turns it can contribute to national food resilience. Based on the simulation result, it was known that the BPNN with two hidden layers is able to predict hydro climatological dat...
Agronomy, 2022
Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected para...
International Journal of Hydrology Science and Technology, 2012
Crop evapotranspiration is the most important parameter for management of irrigation systems in greenhouses. This study was conducted to determine the evapotranspiration of cucumber, tomato and peppers, using micro-lysimeter during seven months in a greenhouse located in central region of Iran. Reference evapotranspiration estimated using drainage lysimeters and the water balance of soil micro-lysimeters was determined using the gravimetric method. To find the relationship between meteorological data and crops height with crops evapotranspiration, artificial neural networks (ANNs) and genetic algorithms-ANNs (GA-ANNs) were used. The results indicated that both models had a quite good agreement with the actual evapotranspiration of crops, but the GA-ANNs model will respond better than the ANNs model.
Estimating wheat and maize daily evapotranspiration using artificial neural network
Theoretical and Applied Climatology, 2018
In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RH max), minimum relative humidity (RH min), average relative humidity (RH ave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ET C) values calculated by ET C = ET 0 × K C equation (ET 0 from Penman-Monteith equation and K C from FAO-56, ANN C) were used as outputs, and for the other five structures, the ET C values measured by weighing lysimeter (ANN M) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN 1 C (8-4-2-1), with T max , T min , RH max , RH min , U 2 , n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R 2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN 5 C (5-2-3-1), with T max , T min , Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ET C used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.
Artificial Neural Network Models to Predict Wheat Crop Evapotranspiration
The development of Artificial Neural Network (ANN) models for prediction of wheat crop evapotranspiration using measured weather data and lysimeter measured crop evapotranspiration (ETc) for Delhi is described. Eleven meteorological variables were taken into consideration for this study. ANN models were developed in MATLAB© with different network architectures using Feed Forward Back Propagation (FFBP) and Elman Back Propagation (EBP) algorithms. The total length of data record used was 744, out of that 60% was taken for model training, 20% for model testing and remaining 20% for model validation. Training and testing data sets were used for model development purpose, while validation data set was used for model evaluation. The ANN modelling strategy having back propagation learning algorithm, log-sigmoid transfer function and model input strategy-1 exhibited better results with Nash-Sutcliffe Coefficient (E) and Root Mean Square Error (RMSE) of 0.972 and 0.498 mm for development data set and 0.776 and 1.334 mm for evaluation data set, respectively. FAO Penman-Monteith method was also used to estimate evapotranspiration. Comparison of the ANN predicted ETc and FAO Penman-Monteith estimated ETc with lysimeter values showed that the ANN predicted ETc was more close to the lysimeter measured values.
2019
Knowledge of exact amount of water required by different crop in a given set of climatological condition of a region is great help in planning of irrigation scheme, irrigation scheduling, effective design and management of micro irrigation systems. Crop water requirement is generally estimated by multiplying the reference evapotranspiration (ETo) by crop coefficient. The Penman–Monteith FAO 56 (P-M) model is recommended for estimating ETo across the world. However, the use of the P-M model is restricted by the unavailability of input climatic variables in many locations and the option is to use simple approaches with limited data requirements. In the current study, linear regression (LR) and Artificial neural network (ANN) techniques were used for estimating ETo in a semi-arid environment of Rahuri region Maharashtra, India. The four types of LR and ANN models were developed by varying the independent variables viz., Model1 (evaporation), Model2 (Tmax and Tmin), Model3 (Tmax, Tmin a...
Engenharia Agrícola, 2015
Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing th...
Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling
Sensors
Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic ...
Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network
2021
Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods P...