Crop Yield Prediction Using Deep Neural Networks

Abstract

Agriculture is undergoing a metamorphosis due to several environmantal and scoal factors. Due to challenges such as global warming, intermittent rainfall patterns and eroding nutrient values of soil, crop yileds have become more upredictable in the last decade. This has resulted in famines, armer suicides and deaths due to hunger. Thus, one of the key objectives of the world health organization is to provide food security globally and also help the agriculture community as a whole whith special emhpasis on low income group countries. This has made crop yield forecasting extremely important. As the crop yield depends on several factors which are highly uncorrelated in nature, hence machine learing based appraoches have been employed for the purpose. In this paper a deep neural network approach has been proposed along with the discrete wavelet transfrom to forecast crop yields. The wavelet transform has been used as a filtering techniques to remove local disturbances from the data, and deep neural networks have been used for pattern recognition and forecasting. The evaluation of the proposed system has been evaluated in terms of the mean absolute percentage error, accuracy and regression. It has been found that the proposed work outperforms existing baseline techniques in terms of the accuracy of forecasting.

Introduction

I. INTRODUCTION

One of the main goals of the world health organization (WHO) is the food security programme which aims a proving food to everyone in the world so as to eradicate deaths due to hunger. This is however challenging due to factors such as explosive population increase, global warming, unprecedented climate changes, eroding soil nutrient values, urbanization, conversion of farmland for industrial uses, mass exodus to urban areas to seek livelihood, decling investments in staple crop production, increase in food costs  etc. Thus it becomes extremely challenging to ensure food security. As per the statistics of WHO, almost 25,000 people die of hunger each day (Holmes, 2020) . It is estimated that a child dies of hunger every 10 seconds and around 3.1 million children die of hunger and malnutirtion each year. The worst hit areas are the Sub-Saharan region in Africa and Asian Countries where the deaths due to hunger are staggeringly large. This leads to the motivation of the WHO to eradicate hunger related deaths by 2030 (Moseley and Battersby, 2020). The situation needs meticulous planning and statistical analysis so as to eradicate hunger related deaths. Crop yield forecasting is one of the key component for the purpose which can render insights into the expected yields therby helping authorities to plan for storgae, distribution and supply of surplus to the needy. Crop yiled forecasting is however channenging due to its dependability on several factors such as time of the year, crop type, amount of rainfall, temperature, type and condition of soil etc (Klompenburg et al., 2020). Attaining maximum crop yield with minimum production cost remains the main goal of crop yield production (Elavarasan and Vincent, 2020) .  There happen to be many challenges associated with the crop yield prediction method. The domain of artificial intelligence has helped in understanding and analyzing the agricultural based markets. Early detection of the problems related to crop yield production can help in quick resolution and aid in increasing yield profit. Predictive methods can be implemented to reduce losses under unforeseen circumstances. Moreover, the prediction methods can be utilized to know the favorable time for growing conditions. Different weather conditions have different kinds of impact on the overall crop yield of a particular area (Dang et al, 2020).

There has been a tremendous growth of artificial intelligence and machine learning in the recent years. The agro based systems and industries have also witnessed an increased adoption of these technologies. This domain has been a prominent area of research for accurate prediction crop yield (Nigam et al, 2019). With the use of meteorological data, it is quite efficient to predict the weather and pest impact on the crops. Several factors affect the yield of crops in some or the other way. For farmers, the crop yield and productivity is of vital importance. Weather conditions are one of the key influencers for the crop yield production. Different types of crops have different factors that impact the respective yield. Hence the motivation behind the research is to evaluate crop yield prediction techniques using the concepts of machine learning. In this paper, machine learning based techniques are analyzed  and a model employing data pre-processing and ensemble learning is proposed for crop yield forecasting.

II. METHODOLOGY

The main challenge pertaining to the forecasting of crop yield lies in the fact that crop yields are affected by several variables which often show a very little correlation (Gopal and Bhargavi, 2019). Hence it is necessary to design a forecasting which can identify the patterns in the seemingly random data, be able to remove the noisy component around the baseline and forecast the crop yield with high accuracy and low error (Hird J and McDermid, 2009). To attain the objective of high forecasting accuracy and low or moderate number of training iterations, it is necessary to focus on two fundamental aspects:

1. Pre-Process the data so as to remove the noisy component along the baseline.
2. Design an appropriate machine learning alorithm which can find patterns in complex time series data.

Thus the first part of the methodology focusses on the pre-processing part to remove the noisy part and filter the data so as to facilitate training.

A. Data Pre-Processing

The pre-processing is done employing the discrete wavelet transform which acts as a recursive filter to filter out local disturbances and noisy nature of the raw data. This step helps in pattern recognition (Khandelwal et al, 2015).  The recursive filtration using the wavelet transform for ‘ith’ level scaling factor can be expressed as (Nury et al, 2017):

II. SIMULATION RESULTS

The simulations have been performed on MATLAB 2020a with an i5 9300H CPU with a clock speed of 2.4GHz and available RAM of 8GB. The first part of the experiment entails data pre-processing so as to remove the noise and disturbance effects form the raw data. For the purpose the wavelet transform has been employed. A three level decomposition of the raw data has been performed in which the detailed co-efficient values are discarded and approximate co-efficient values are retained so as to filter out the noise effects. The approximate co-efficient values along with the detailed co-efficient values are used to train an ensemble neural networks. The data is split in the ratio of 70:30 for training to testing. The parameters used for training are time, rainfall, moisture, humidity, temperature and soil type. The data has been acquired from Kaggle.

Table 1.

Statistical analysis of raw data ‘s’

Table 2.

Statistical Analysis of Approximate Co-efficients, ‘Ca’

Table 3.

Statistical Analysis of Detailed Co-efficients, ‘Cd’

Table 4.

Summary of Simulation Results.

Table 5.

Comparison with existing techniques.

Figure 1 depicts the data in the Matab workspace after loading the data. The data is accessible in the Matab workspace for analysis.  The dependent variables (feature) along with the target variable (yield) is decomposed to 3 levels of DWT. This would mean an approximate co-efficient value denoted by ‘a’ and three detailed co-efficient values ‘d1’, ‘d2’, ‘d3’ would be obtained through the decomposition. The metrics of decomposition are chosen as maximum value of variables, minimum value, mean, median, standard deviation, mean absolute deviation, L1 norm and L2 norm. The analysis of the parameters helps us in understanding the effect of the wavelet decomposition on the data cleaning process. ‘S’ corresponds to the original data stream, C_A corresponds to the approximate co-efficient values while C_D corresponds to the detailed co-efficient values. Tables 1, 2 and 3 enlist the values of decomposition. An evaluation of the decomposition metrics and insights into the data are presented subsequently.cThe statistical analysis for the approximate value indicate the following:

1. The approximate co-efficient values contain the majority of the information of the data stream
2. Retaining the approximate co-efficient values help in retaining the maximum statistical information of the data.
3. The iterative process of retaining the approximate co-efficient values makes the histogram approach the actual data.

Moreover, the significance of the analysis lies in the fact that it tells about the statistical information contained in the data stream, the decomposed approximate values, the decomposed detailed values and the synthesized data stream. The histogram analysis implies the fact that as the number of levels increases, the local disturbances in the data are eventually removed.  From table I, II and III, it can be clearly seen that as the number of decomposition levels increase, the approximate co-efficient values tend to align towards the original data stream in terms of statistical characteristics

a. The detailed co-efficient values however tend to deviate from the actual data stream as the number of levels increase. This clearly indicates that if the approximate co-efficient values are retained and the detailed coefficient values are discarded, then the amount of local variations and disturbances can be removed.

b. The wavelet transform can be used to maintain monotonicity in local intervals so as to make the training more effective for the neural network.

The above three points indicate that the wavelet decomposition along with the time series prediction can enhance the accuracy of prediction and also increase the regression for large data sets.

Figure 2 represents the curves for the predicted and the forecasted values. The red curve depicts the forecasted or predicted values. The blue curve depicts the actual values. It can be clearly concluded that the value of the regression is clearly related to the accuracy of prediction for the system. A comparative analysis of the previous and proposed work in terms of the evaluation parameters is given in table 4. Figure 3 depicts the regression for training, testing, validation and overall cases. A summary of the obtained results is presented in table 4.

A comparative analysis of the proposed work with contemporary techniques shows that the proposed technique outperforms the existing techniques in terms of accuracy of prediction. This can be attributed to the combined data filtration and ensemble learning approach adopted in this work.

Conclusion

It can be concluded that crop yield prediction is a critically important forecasting problem trying to address food security in the world. However, it is challenging to accurately forecast crop yields since the data is generally random and complex and the yield depends on multiple parameters. The proposed system uses a two step approach in which the data is first filtered and secondly a deep neural network employing back propagation is used for pattern recognition. The performance of the system has been evaluated in terms of the mean square error, mean absolute percentage error, regression and accuracy. From the results, it can be observed that the system trains in low number of iterations and also achieves low MAPE value. A comparative analysis with respect to previous work also shows that the proposed technique outperforms the existing technique in terms of prediction accuracy.

References

[1] Bhosale S, Thombare R, Dhemey P, Chaudhari A (2018). Crop Yield Prediction Using Data Analytics and Hybrid Approach, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-5. [2] Dang C, Liu Y, Yue H, Qian J (2021). Autumn Crop Yield Prediction using Data-Driven Approaches:-Support Vector Machines, Random Forest, and Deep Neural Network Methods, Canadian Journal of Remote Sensing, Taylor and Francis, 47(2), 162-181. [3] Elavarasan D, Vincent P (2020). Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications, IEEE Access 2020, 8, 86886-86901. [4] Fernandez-Ordoñez Y, Soria-Ruiz J (2017), Maize crop yield estimation with remote sensing and empirical models, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3035-3038. [5] Gopal P, Bhargavi R (2019). A novel approach for efficient crop yield prediction, Computers and Electronics in Agriculture, Elsevier, 165, 104968. [6] Hajiabotorabi Z, Kazemi K, Samavati F, Ghaini F (2019). Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series, Expert Systems with Applications, Elsevier 2019, 138, 112842. [7] Hird J, McDermid G (2009). Noise reduction of NDVI time series: An empirical comparison of selected techniques, Remote Sensing of Environment, Elsevier, 113(1), 248-258. [8] Holmes J. (2020). Losing 25,000 to Hunger Every Day. Retrieved from UN Chronicle: https://www.un.org/en/chronicle/article/losing-25000-hunger-every-day [9] Huang X, Huang G, Yu C, Ni S, Yu L (2017). A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging, Journal of Field Crops Research, Elsevier 211, 114-124. [10] Islam T, Chisty T, Chakrabarty A (2018). A Deep Neural Network Approach for Crop Selection and Yield Prediction in Bangladesh, 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 1-6. [11] Khandelwal I, Adhikari R, Verma G (2015). Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition, Procedia Computer Science, Elsevier, 48, 173-179. [12] Klompenburg T, Kassahun A, Catal C (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics and Agriculture, Elsevier 2020, 177, 105709. [13] Madan R and Mangipudi P (2018). Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN, 2018 Eleventh International Conference on Contemporary Computing (IC3), 1-5. [14] Moseley W, Battersby J (2020). The vulnerability and resilience of African food systems, food security, and nutrition in the context of the COVID-19 pandemic. African Studies Review, Cambridge Publications, 63(3), 449-461. [15] Nigam A, Garg S, Agrawal A, Agrawal P (2019). Crop Yield Prediction Using Machine Learning Algorithms, 2019 Fifth International Conference on Image Information Processing (ICIIP), 125-130. [16] Nury A, Hasan K, Alam M (2017). Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh, Journal of King Saud University – Science, Elsevier, 29(1), 47-61. [17] Rhif M, Abbes A, Farah I, Martínez B, Sang Y (2019). Wavelet transform application for/in non-stationary time-series analysis: a review, Applied Sciences, MDPI, 9(7), 1-22. [18] Tealab A (2018). Time series forecasting using artificial neural networks methodologies: A systematic review, Future Computing and Informatics Journal, Elsevier, 3(2) 334-340.

Copyright

Copyright © 2022 Akash Malviya, Prof. Dilip Singh Solanki. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.