Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results indicate that the GA-BP-NN technique offers a slightly better yield correlation. Our main conclusion is that this method would offer growers more confidence in making a better nitrogen decision during the season than currently available models due to more precise yield predictions. It also can also contribute to possibly better grain marketing decisions later in the season

Armstrong, Leisa

Leng, Jinsong

Neuhaus, Andreas

English

Research Online @ ECU

Edith Cowan University Research Online ECU Publications Post 2013 1-1-2014 A network that really works - the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia Jinsong Leng Edith Cowan University, j.leng@ecu.edu.au Andreas Neuhaus Leisa Armstrong Edith Cowan University, l.armstrong@ecu.edu.au Follow this and additional works at: https://ro.ecu.edu.au/ecuworkspost2013  Part of the Agricultural Science Commons, and the Artificial Intelligence and Robotics Commons Leng, J. , Neuhaus, A., & Armstrong, L. (2014). A network that really works - the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia. Proceedings of Asian Federation for Information Technology in Agriculture. (pp. 298-306). Perth, W.A. Australian Society of Information and Communication Technologies in Agriculture. Available here This Conference Proceeding is posted at Research Online. https://ro.ecu.edu.au/ecuworkspost2013/852 A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia  Jinsong Leng1, Andreas Neuhaus2, Leisa Armstrong1 1School of Computer and security Science, Edith Cowan University, Perth, Australia  2Fertiliser Services, CSBP Limited, Kwinana, Perth, Australia Email: j.leng@ecu.edu.au  Abstract  Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results indicate that the GA-BP-NN technique offers a slightly better yield correlation. Our main conclusion is that this method would offer growers more confidence in making a better nitrogen decision during the season than currently available models due to more precise yield predictions. It also can also contribute to possibly better grain marketing decisions later in the season.    Key Words: artificial neural networks, yield, nitrogen management   Introduction  Western Australia (WA) produces currently more than 40 percent of the national wheat crop. However, broad-acre soils in WA are inherently low in soil mineral nitrogen (N) and organic N and thus N is required to lift production on these soils. Management decision tools for N applications are available, but are lacking accuracy to predict N requirements often as a result of inaccurate yield predictions.  Yield predictions are based on many interdependent factors such as rainfall, soil properties, plant health, plant density etc. Those factors are dynamic. One of those important factors is i.e. soil nitrogen, which can change in its plant available amount quickly during the season. The additional problem is that growers want to know about their yield forecasts as early as possible to gauge crop N demand and yield responsiveness to N fertiliser.  Traditional data modelling techniques have been worked into N decision support systems (DSS) for a long time in WA. They do require continuous research and expertise to develop and maintain the underlying quantitative cause-effect relationships. Recently AI applications that mimic our brain capability to learn have found their way into DSS and offer a more flexible alternative approach for easier, more cost-effective maintenance without compromising the quality of the decision support. Soil moisture data, for example, has been estimated successfully for irrigation scheduling using ANN in paddy fields in West Java, A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 298Indonesia (Arif et al., 2012). Furthermore, an ANN technique has been applied to predict longan yields with an average of 80-83% accuracy in northern Thailand and was then incorporated into a mapping program, ArcMap (Thongboonnak and Sarapirome, 2011). ANN could be applied to a wide range of different agricultural DSS. This study examines two ANN techniques for yield predictions of wheat in WA using local soil, plant and rainfall data.   In dryland agriculture, rainfall is usually one of the most yield limiting factors. It also determines N mineralisation and N availability. Rainfall in this Mediterranean type climate is not only variable in its intensity, but it is also geographically and temporally highly variable. This affects the N cycle and hence the yield predictions and N status will have to be based on such input factors. Figure 1 of the south-west in WA demonstrates the seasonal difference of the effect of summer rainfall on soil nitrate values due to N mineralisation in 2013 (left, with summer rain) compared with 2014 (right, no or little summer rain). Images are obtained from more than 50,000 topsoil samples for each map.   Figure 1: Seasonal difference in nitrate values at the start of the 2013 season (left) compared with the 2014 season (right).  In this study, yield predictions are derived from multi-factorial relationships. The relationships have been captured in the matrixes of different ANN techniques that have been applied. Furthermore, the importance of each factor on yield has been statistically tested to understand the main forces behind yield predictions.   Material and Properties  CSBP field trial data has been used to compile the database. The sample size comprises of 522 data points and variables of those 24 N responsive field trials include summer and growing season rainfall, cultivar, soil texture, location, soil organic carbon (%), soil pHCaCl2, previous crop in rotation, sowing and emergence date, sampling date for plant tissue analysis, plant N (%), plant NO3 (mg/kg), shoot dry weights and biomass. For a more detailed   A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 299description of the data see Neuhaus et al. (2014). Initially this dataset for testing and validating has been determined using a principal component and cluster analysis. The workflow for yield predictions followed a 2 step process in which firstly the N status was estimated from soil and plant data and secondly this N status combined with rainfall and biomass data estimated yield.  As a first statistical analysis, a principle component analysis (PCA) was conducted on the data to investigate the importance of management factors on yield. Secondly, a cluster analysis was performed to identify pattern in the data.  The principle component biplot in Figure 2 shows principle component 1 (PC1) versus principle component 2 (PC2), describing the principal component loadings which represent the traits used to explain the yield variance for each dimension (ie for horizontal axis, PC1 shows that N%, NO3, plant age, and above-ground plant dry weight explain the majority of the 24.6% in this dimension). After adjustment for these traits (ie PC1), PC2 shows that the largest components are pHCaCl2, seeding rate, the previous crop and emergence day, which explain a further 20.1% yield variance. Thus plant vigour is a more important trait than the other traits of selected management conditions. Figure 2: PCA of yield related management factors.  The cluster analysis showed the highest weights for soil organic carbon (%), which was 1.39, soil pHCaCl2, which was 0.41 and location, which was 0.39. Soil organic carbon, soil pHCaCl2 and location (climate) affect N mineralisation and therefore it is not surprising to observe this pattern.  A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 300 Methods  The back-propagation neural network (BPNN) includes input layer, hidden layer(s), and output layer, as shown in Figure 3.   Figure 3: The structure of BPNNs The number of nodes in each hidden layer is computed as: S = round(sqrt(inputnum + outputnum))+10  where inputnum is the number of inputs (features), and inputnum is the number of output.  In Figure 3, we can find the structure of BPNNs includes two hidden layers and each hidden layer has 14 nodes.  The basic BPNNs algorithm adjusts the weights in the steepest descent direction (negative of the gradient). Although the function decreases most rapidly along the negative of the gradient, it may not produce the fastest convergence, so the steepest descent is often slow in convergence. To overcome hose drawbacks, the conjugate gradient algorithm is employed to search along conjugate direction, so as to produce generally faster convergence than steepest descent one.   As indicated above, the performance of the BPNNs algorithm is very sensitive to the proper setting of the initial weights and parameters. In general, the weights of BPNNs are initialized through a random process, which means the performance and output could be different from each running the algorithms.  So it is of great importance to obtain the ‘best’ performance by choosing the optimal initial weights of BPNNs.   The task of BPNNs is to be divided into two steps: 1). Find the optimal weights of BPNNs; 2). Build the BPNNs model for yield predictions and nitrogen management.  Due to the inherent nature of complexity, it is almost impossible to find the optimal weights of BPNNs using the traditional analytic methods. In this work, two techniques are employed and implemented to optimize the initial weights of BPNNs, i.e., particle swarm optimization (PSO) and genetic algorithms (GA). Both PSO and GA are able to evolve with some randomness towards optimal solutions.  PSO (Kennedy and Eberhart, 1995) is one of swarm intelligence (SI) techniques, including ant colony system (Dorigo and Gambardella, 1997), artificial bee colony algorithm A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 301(Karaboga and Dervis, 2010), etc. PSO is an optimal computation technique by mimicking the behaviour of bird flocking. In PSO algorithm, each particle represents a potential solution, and is linked to a certain degree of fitness that is determined by a fitness function. The position and velocity of each particle are changing to the better solution over time by estimating its fitness.  In a D-dimensional space, the group that consists of n particles can be represented as X = (X , X       , and the i-th particle has a D-dimensional vector X   (X  , X          that represents a potential solution. The velocity of each particle in each dimension is defined as 1 2( , ,...., )i i i iDV v v v , the best position values of individual and group are represented as 1 2( , ,...., )i i i iDP p p p and P  = (p    p      p  ). The velocity and position are updated in terms of the best values of individual particle and group, defined as:  ( 1)  tidV ( ) ( ) ( )1( ) ( )2[0,1] ( )[0,1] ( )t t tid id idt tgd idV U p xU p x                                            (1)   ( 1) ( ) ( 1)t t tid id idX X V                                                                                                   (2)  where U [0,1] samples a uniform random distribution, t is a relative time index, 1 and 2are weights trading off the impact of the local best and global best solutions’ on the particle’s total velocity.   GA searches for the optimal solution in a different way. As indicated in Figure 4, GA finds the optimal solution in an iterative manner:   1. Initialize a population of candidate solutions;  2. Evaluate each individual in the population with the fitness function.  3. The individuals are evolved through the following process:  Elitist selection;  Crossover;  Mutation.  4. Check the termination condition(s):  Terminate if the stopping criteria are met;  Otherwise, generate a new population, and go back to step 2.  During the evolution, the best solution in the population is always retained in next population. So this strategy can guarantee the convergence of the GA algorithms.       A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 302Results  The ‘optimal’ weights of BPNNs can be obtained, with PSO or GA techniques. The related fitness curves of PSO and GA are plotted as below:       Figure 4: Fitness Diagram of PSO-BPNNs      Figure 5: Fitness Diagram of GA-BPNNs  From the diagrams in Figure 4 and 5, it is clear either PSO or GA is able to converge to ‘optimal’ fitness in about 20 iterations.                 Figure 6: PSO-BPNNs for N status data      Figure 7: GA-BPNNs for N status data  A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 303  For predicting the outputs of the N status with PSO-BPNNs, the prediction errors are plotted in Figure 6, the mean squared error (MSE) is 7.0882, and the correlation coefficient is 0.6775. For predicting the same outputs with GA-BPNNs, the prediction errors are plotted in Fig. 7, the mean squared error (MSE) is 7.1356, and the correlation coefficient is 0.6718.                Figure 8: PSO-BPNNs for yield data        Figure 9: GA-BPNNs for yield data For predicting the yield outputs with PSO-BPNNs, the prediction errors are plotted in Figure 8, the mean squared error (MSE) is 0.6103, and the correlation coefficient is 0.8772. For predicting the same yield outputs with GA-BPNNs, the prediction errors are plotted in Figure 9, the mean squared error (MSE) is 0.5359, and the correlation coefficient is 0.9043.  The difference in correlation factors from the dataset for the N status (r2 = 0.67) and the dataset for yield (r2 = 0.88 and 0.90) reflects the sensitivity of yield predictions to rainfall and biomass as the overriding and dominant factors. Rainfall is not manageable by the grower, but biomass is to some extent, although it depends largely on rainfall too.   Discussion  This study found statistically significant yield correlations using back propagation neural networks and thereby agrees with other AI studies which also found highly significant yield predictions, such as findings from Panda et al. (2010) and Thongboonnak and Sarapirome (2011). In this study, the GA-BP-NN has produced reliable and slightly better correlations than the PSO-BP-NN method. Furthermore, this study complements earlier work which showed that an artificial neural network (ANN) using the “Leave Out One Cross Validation (LOOCV)” technique is an accurate technique to forecast yield with optimum N versus growers yield at the time of plant sampling with no further N applications (Neuhaus et al., A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 3042014). Yield predictions were about 30% more accurate than expert models such as APSIM/Yield Prophet that draw on traditional modelling techniques.    French and Schultz (1984) emphasised the importance of rainfall for yield predictions in wheat grown a Mediterranean-type climate. Oliver et al. (2009) added some simple modifications like for example soil texture to adjust the rainfall data for yield predictions. However, even with those adjustments the correlation is well below the yield correlation from the GA-BP-NN used in this study. The better correlation from the GA-BP-NN method may be contributed to either the computational method and/or the inclusion of plant testing data.  Conclusion  It can be concluded from this study that the GA-BP-NN method, if available as a decision tool in WA, would improve N management during the season and thereby the profitability and sustainability of N inputs into the farming system. It also can also contribute to possibly better grain marketing decisions later in the season.     References  Arif C, Setiawan B I, Mizoguchi M and Doi R (2012). Estimation of soil moisture in paddy field using Artificial Neural Networks. International Journal of Advanced Research in Artificial Intelligence, Vol. 1 No. 1: 17-21.  Dorigo M and Gambardella LM (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1: 53-66.  French RJ and Schultz JE (1984). Water use efficiency of wheat in a Mediterranean-type environment. I. The relation between yield, water use and climate. Australian Journal of Agricultural Research, 35, 743-764.  Karaboga and Dervis (2010). Artificial bee colony algorithm. Scholarpedia 5 (3): 6915.  Kennedy J and Eberhart R (1995). “Particle Swarm Optimization“. Proceedings of IEEE International Conference on Neural Networks IV. Pp: 1942-1948.  Neuhaus A, Armstrong L, Leng J, Diepeveen D and Anderson G (2014). Integrating soil and plant tissue tests and using an artificial intelligence method for data modelling is likely to improve decisions for in-season nitrogen management. 2014 Agribusiness Crop Updates conference, 24-25 February 2014, Perth, Western Australia.  A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 305Oliver YM, Robertson MJ, Stone PJ and Whitbread A (2009). Improving estimates of water-limited yield of wheat by accounting for soil type and within-season rainfall. Crop and pasture Science, 60, 1137-1146.  Panda SS, Ames DP and Panigrahi S (2010). Application of vegetation indices for agricultural crop yield prediction using neural networks techniques. Remote Sensing, 2, 673-696.  Thongboonnak K and Sarapirome S (2011). Integration of Artificial Neural Network and Geographic Information System for Agricultural Yield Prediction. Journal of Scientific Research, Vol. 18, No. 1: 71-80.   A network that really works – the application of artificial neural networks to improve yield predictions and nitrogen management in Western AustraliaProceedings of AFITA 2014 - Copyright ASICTA Inc. 306

A network that really works - the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia

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A network that really works - the application of artificial neural networks to improve yield predictions and nitrogen management in Western Australia

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