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Amalgamation of Machine Learning Algorithms for Crop Yield Prediction

Agriculture is India’s prime occupation. In Indian economy agriculture plays a major role by means of providing more employment opportunities for the people. In order to provide food to the huge population of India, agriculture sector needs to maximize its crop productivity. This research work presents an approach which uses different Machine learning (ML) techniques by considering the various parameters of cultivated crop to predict the best yield. Further in this Multiple Linear Regression (MLR) technique and artificial neural networks (ANN) are used to make a brief analysis for the prediction crop yield. With the above idea a new model was created, and from this numerical results were obtained. The accuracy and efficiency of the method has been explored and results from the artificial neural network and regression methods were obtained and compared.

The motivation behind this research work is to investigate appropriate machine learning techniques, which are handy to solve agricultural problems. Machine Learning comes into the picture when problems cannot be solved by means of conventional approaches. This study analyzes the various factors of agriculture, to evolve a new prediction model and helps the farmer's in easy decision making there by increasing the crop yield.
Machine learning uses archive data to gain knowledge and makes deeper insights in the data for output predictions. The excelling in the compilation of good dataset gives the better accuracy in predictions. It has been contemplated that machine learning techniques such as regression and classification gives better results than various other statistical models [3].

A. Multiple linear regression.
In statistics dependent variables are termed as predictant and independent variables are termed as predictors. The correlation between a predictant and a predictor is called linear regression but the correlation between a predictant and one or more predictors is called multiple linear regression (MLR). The least square based methods are widely used in MLR techniques. Crop yield depends on all these ecological factors so, which is considered as dependent variable. In this crop yield prediction model, Production is the predictant and which is used in the MLR technique [4].

B. Artificial neural network
A human brain encompasses millions of neurons, which are used to communicate with each other, and send information. Neural networks replicate the central nervous system of humans [1]. Artificial neural networks (ANN), in fact, simulate a part of brain functions. One of the important concepts in machine learning is Artificial Neural Networks. Configure and train the ANN, is cumbersome and time consuming process because of its complex nature but once trained, it becomes very fast in executing application.

C. ANN structure
The artificial neurons have capability to process colossal amounts of data, and deduce to conclusions by deriving a pattern from it. Due to the speedy competence of neural networks in processing the training datasets, makes them to use in classification. Input, Hidden and Output are the three layers, which constitute the artificial neural network [5].


Input layer: Get the raw data that has been fed to the network as input. A desired output is also given along with input to verify the correctness of the neural network.  Hidden layers: In this layer, weights and attributes multiplied then all such resultants are aggregated to generate the output.  Output layer: When dissimilarity between the desired and the actual output are found, then the feedback is given to the hidden layer from output layer to obtain better accuracy.
The structure of ANN has been displayed in

D. Neural networks training mechanism
The flow of data is propagated in forward direction and is always in the direction of output. There is no feedback mechanism, available in forward propagation. So, verifying the precision of output is extremely tedious. Actual and expected output was repeatedly compared to train the back propagation. The difference between actual and expected output is given to the error function as input. The error functions are responsible for the changing of weights in the hidden layer. This process will be repeated again and again to reduce the difference to minimum. Feed forward and back propagated artificial neural networks are used to get accurate crop yield forecast and helps the farmers in devising better decisions. ANN has been enforced for predicting better crop yield on the basis of various predictor variables [6]. The blue print of this paper is given below: Section II describes the literature survey in the field of crop yield prediction. Section III discusses about the proposed methodology for predicting the yield of crops. In Section IV, evaluates the proposed methodology and discuss the experimental results. In Section V, concludes the methodology for the yield of crops.

II. LITERATURE REVIEW
ANN is more powerful, when compared to conventional linear and simple nonlinear analyses. For yield estimation, ANN uses self adaptive method. In a special network structure a nonlinear response function has been used to study the cumbersome functional connection between input and output training data. Once the ANN model has been trained, it remembers functional connection and uses it in the later part of the calculations. ANN has the ability of self learning, so it has been widely used to create nonlinear and complicated models [6] [7]. During the preparation process agriculturalist wants easy and meticulous assessment methods to forecast the crop yields [8]. It is necessary to study the effectiveness of ANN models, before forecasting the crop yield in the usual weather conditions of mountain regions [9]. The effectiveness of MLR models are compared with ANN models by evaluating its performance in relation to various parameters [10]. Hemageetha discuss on the crop yield prediction by using soil parameters like organic matter, Soil pH, Electrical Conductivity and moisture. Naive Bayes algorithm achieves 77% of accuracy in soil classification. Apriori algorithm was used by combining the soil and crop details to attain the highest yield. Naïve Bayes, J48 and JRIP algorithm`s accuracy during classification were compared and listed [11]. Ankalaki, Chandra & Majumdar discuss about the clustering algorithms like DBSCAN and AGNES. Crop yields are forecasted by using MLR technique and for each crops a new formula has been devised [12].
Raorane & Kulkarni appraises several data mining approaches like ANN, Decision Tree, Regression Tree, Bayesian network, SVM, k-means to improve the agricultural crop productivity. Data mining techniques are mainly used to make classification of data [13]. Gonzalez-Sanchez, Frausto-Solis & Ojeda-Bustamante raises a substantial problem in agricultural planning is accurate yield estimation method. To achieve realistic solutions for this problem is Machine learning (ML) approach. A survey was conducted on ML methods used for yield prediction and to find the most meticulous method out of it. In this paper author noted the dissimilarity in the accuracy of crop yield prediction between ML with linear regression techniques [14].

III. METHODOLOGY
By analyzing the large datasets, Machine Learning algorithms classify and establish patterns from it. Based on available data, the crop yield production was analyzed. Crop yield Prediction by using Machine Learning technique will increase the crop productivity.
In this research the statistical methods were applied to get the predictive accuracy. For the estimation of crop yield analysis, MLR technique and ANN were taken up.

A. Overview of dataset
In any kind of research the data should collect properly, which makes the model more efficient; otherwise it can cause imprecise results. The dataset considered in the present study is collected from various sources like Department of Statistics and Agriculture, National Informatics Centre, data.gov.in and merged into one, such that a dataset sufficiently large enough for the study is created. In order to increase the purity of collected data is to be pre-processed by means of removing the outliers from it.

Variables used for crop yield prediction:
In this study the variables depicted for predicting the crop yield are described by us are Yearly Rainfall (YR), the cultivated Area (AR) is specified in Million Hectare, Food Price Index(FPI), Production (PRO) is mentioned in Million Tons and Crop Yield(CY) is specified in kilograms per hectare. Crop Production details in India are presented in the table I from 2001 -17.

B. Multiple Linear Regression
If the variables used in regression function is linear then that model is called as linear regression model otherwise it is non-linear model. The relationship between one dependent variable and more than one independent variables is called multiple linear regression. Let us take dependent variable as y, independent variables as x i and set of unknown parameters as b i which is associated by the regression function.
Multiple linear model General formula is as in (1) In this research work, the independent variables YR, AR, FPI, PRO and dependent variable CY are used for the MLR analysis to determine the relationship among them. Here the crop yield is influenced by environmental factors such as rainfall etc., The dissimilarity between some data points and the regression line is called residuals otherwise known as "errors". The residual (e) is calculated by subtracting dependent variable`s observed value (y) from the predicted value (y`) is shown in the equation (2). In general each data point used has one residual.

e = yy`
-------(2) The deviation of the predicted values from the observed values are squared and then summed is called residual sum of squares. The squared value of correlation coefficient is taken as R 2 and is used to evaluate the strength of any linear model. The R 2 value corresponds the dependent variable`s variability, which should always lie within the range 0 and 1. The data in the Table 1 is showing the association between the independent variables YR, AR, FPI, PRO and dependent variable CY after applying the MLR process.

C. Artificial Neural Network
Inputs to the ANN are represented by x0, x1, x2, x3...x(n) and each of these inputs are multiplied by a connection weight or synapse which are represented as w0, w1, w2, w3….w(n) . The Weight represents the particular nodes strength. A bias value b allows moving the activation function in either direction. To generate a result, these products are summed and fed to an activation function. Finally this result is sent as output. This mathematically represented in (3). Later apply activation function ( ∑ x i .w i )

IV. RESULTS AND DISCUSSION
The various regression statistics obtained from the tool XLMiner are listed in table II. Obtained value of R 2 through the regression analysis is larger than 0.5, means the bonding between the dependent variable and the independent variable is more.

A. Predictions using MLR and ANN
The statistic value R 2 for the MLR and ANN was measured to find its accuracy in prediction. The closeness the data to the fitted regression line was calculated by using the statistical measure R 2 is given in equation (4), Where, sum of squared error (SSE), sum of squared total (SST), n, p are the number of observations and regression coefficients respectively. The prediction accuracy of the given model is obtained by calculating the value of R 2 and the value of R 2 is higher, then the prediction by the given model is better. The accuracies or the MLR and ANN models are shown in Table IV.  fig. 4, MLR performs well during training and validation performs better but the ANN performs well in testing. In the present technological era, it is mandatory for any agriculturalist to update them from the traditional approach into the modern scientific approach for doing the agriculture. In this study different parameters were considered for crop yield prediction because in agriculture crop yield prediction is substantial. After comparing the results of MLR with ANN model during test phase shows that ANN has maximum accuracy that is higher R 2 value with minimum prediction error than MLR model. Thus the ANN models predicts crop yield better than MLR model. Present research work is continued by combining more factors like crop diseases, different irrigation patterns, climatic conditions etc. that influences the yield of a crop and these influencing factors were analyzed by using different machine learning techniques. Successful integration of machine learning with agriculture helps the farmers to maximize their yield and optimize the use of available resources.