<figure><img src="image_1.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
<figure><img src="image_2.png" /></figure>

# An Embedded System for Smart Farming Using Machine Learning Approaches

R. Dhaya Sree(*), A. Arun Raja, and S. Jayanthy

Sri Ramakrishna Engineering College, Coimbatore 641 022, India
{dhayasree.2057001,arunraja.a,jayanthy.s}@srec.ac.in

**Abstract.** The agriculture is very essential and important part of human lives and serves as the basis of economical growth. The yield of the crops is dependent on numerous factors and the most important factor which effects the crop production in the soil. Soil has several parameters such as Nitrogen content(N), phosphorous content, potassium content(K), pH, temperature and humidity which are to be analysed in order to provide a view on which crop is suitable for cultivation. In the current research, a relative survey is made on the soil characteristic parameters in order to predict the future crop which can be grown is performed using predictive analysis algorithms. In addition to that the NPK content for each crop is analysed and if there is any deficiency in these contents, suggestions are given in order to improve the deficient content. The embedded system which has sensors and controller is used in the data acquisition process. The analysis is done using the data which are obtained from the sensors like Nitrogen, phosphorous, potassium, pH and temperature which are attached to the field and the sensor data is processed using the LPC2148 microcontroller. The data set are trained using KNN, Gaussian Naïve Bayes, Random Forest, Decision tree, Logistic regression and SVM. Experimental results indicate that Gaussian Naïve Bayes and Random Forest are found to be more efficient with reference to metrics like accuracy, precision, F1 score and recall. And hence the prediction is made using the GNB and Random Forest algorithm and the percentage of accuracy for the algorithms are 97.3% and 98% respectively 88%.

**Keywords:** K-Nearest neighbour (KNN) · Support vector machine (SVM)

## 1 Introduction

Agriculture is one of the pre-eminent and primary activity of every human culture. For wide population of India the major income source is from agriculture. Due to the demand for the production is also increasing steadily. The crop yield is majorly influenced by the soil conditions, metrological conditions and by the use of fertilizers and pesticides. The accurate availability of these information may help the farmers make a good agronomical conclusions and in choosing the particular type of crop which is better suited for growing under the definite conditions. By forecasting the yield from the historical data by including the soil factors like NPK content, pH, temperature and humidity the crop
yield prediction is made. The predictions are also made on the basis of Nitrogen(N), phosphorous(P) and potassium(K) content available in the soil at that time, so that the prediction of the crop can be made on the basis of both the historical and present data. The major type of soil which are aided in the plant growth is the red loamy soil, black soil and alluvial soil and major production of the crops are done on these types of soil, and they also have its own identical parameters and the yield of the crop is supported. So, a proper agricultural planning has to be done in order to attain the maximum yield of crops without the wastage of resources. In India prediction in the crop yield is important as it has the food wastage of 16-20% from the whole production. In that case the prediction in crop yield plays a crucial role. For this a proper crop selection method has to be implemented depending on the environmental and soil conditions, which is made using the machine learning algorithms. The prediction of yield can be made accurately using the classification and regression algorithm. In the proposed project the crop prediction is done on the basis of acquired data of N, P, K content of soil from different fields and also by considering the factors like humidity, rainfall which are associated with particular type of crops. Here the prediction is also done in such a way that it predicts the N, P, K content for the crop and if there is any deviation in the particular content it detects the change and gives suggestion based on the improvisation of the content. For the analysis the N, P, K, pH and temperature values are acquired from the sensors which are attached to the soil and acquired through LPC2148 micro controller. The acquired input data is prepared and given training using the supervised machine learning algorithms like KNN, Gaussian Naïve Bayes, Random Forest, Decision tree, Logistic regression and SVM. Among all these, the coherent classification algorithm is selected depending upon the metrics like precision, accuracy, recall and F1-score. In this analysis Gaussian Naïve Bayes and Random Forest algorithms are found to be efficient with accuracy of 98%.

## 2 Related Works

TMonali Paul et al. (2019) had proposed a system which used the data mining technique and predictive analysis technique for crop prediction. The author has classified the soil into low, medium, high using the mining of data or determining the yield of the crop. The system proposed helps the farmers and soil analyst to determine what type of crop can be sown for yielding a better production. The predictive analysis algorithms like KNN and Naive Bayes are implemented in this method. The usage of small data sets in the system is the major drawback [1].

Shivam Bang et al. (2019) had proposed the method where (ARMA) and Seasonal ARIMA (Auto Regressive Moving Average) models for the temperature upshot. Time series data which are collected id used in determining temperature. They also used the Fuzzy logic system in order to upshot the yield. The least error from the model is taken inorder to predict the yield. The main shortcomings is that they work only on the set range of values rather that discrete values and hence the difference between the true and approximated values that are predicted will be drastic. This model can successfully predict the crop by the records of temperature and rainfall of the preceding year [2].

P. Vaishnavi et al. (2020) has proposed a method which uses classifiers for crop recommendation. The used machine learning algorithms in the proposed method are
KNN and decision tree. This paper mainly aims in determining the type of seed that can be sown in the field. Here KNN algorithm which showed 97% accuracy is used and accuracy boosting methods are used. In the proposed system ensembling methods are not used and that is the major drawback [3].

Arun Kumar et al. (2019) has proposed a system with the SVM's weightage for estimation of crop yield. The proposed system has considered the parameters like rainfall, humidity and climatic conditions in order to prediction of variation in the yield of the crops. ARIMA model used in the system is used to deal the data with time series constraints. The main drawback of the system is that only less parameters like rainfall, temperature and humidity are taken into consideration for the prediction. Here since time series analysis may have less accuracy when comparing to the ensemble technique [4].

Dhivya elavarasan et al. (2021) had proposed the system with feature processing which is done using RNN and DQN is used for self-experimental analysis. Unlike supervised learning methods this DRQN is used to mine independently the non linear mappings between the soil, ground water parameters and climate. However, the proposed RNN based DRL model will cause the gradient parameters to detonate when the time series is longer, this is the main flaw of the proposed method [5].

Mummaleti Keerthana et al. (2021) had proposed a methodology which is based on the Ensemble method of decision tree along with AdaBoost which is a regressor is utilized for increasing the accuracy rate. The main algorithm which is taken into consideration is the decision tree for the crop prediction wherein the decision tree alone does not have accuracy outcome which produces the weaker results and to increase the accuracy rate a regressor is implemented. The elevation of accuracy will give the yield. The major lack in the proposed system is that only one machine learning algorithm is taken into consideration [6].

Saeed Khaki et al. (2019) had proposed a system wherein used neural networks to predict, analyse and compare yield differences. In this the efficiency of the algorithm is explained and also tells how the algorithm is selected suitably. More efficient and accurate results are obtained. Since Neural network has more complexity while implementing they are difficult to use and they are only suited efficiently for image related data rather than numeric data [7].

Y. Jeevan Nagendra Kumar et al. (2019) had proposed a data mining method for enhancing the rate of accuracy. The data set which is considered in the system has the parameters like pH, rainfall, humidity and temperature. During the stage of training a large decision trees are constructed at the end result is got by numerous classes that are used. In this paper the author has proposed two supervised predictive analysis algorithms like random forest and Decision tree. The main short coming in the system is that there is not Ensembling technique used and the proposed algorithm had a less accuracy [8].

The major convenience of the model discussed here is that the crop prediction is done using the different supervised predictive analysis algorithm like KNN, Naïve Bayes, Random Forest, decision tree and logistic regression. The classifier algorithms utilized here will be helpful in identifying and predicting the crop type which can be grown in accordance with the soil parameters. Here numerous factors which influence the crop production are taken into account and the analysis is also extended in such a way that for
the particular crop if the amount of N, P, K is low, suggestion will be given based on how to improve the particular content. Since the proposed project has all the soil condition parameters analysed it can be used in the soil management also.

# 3 Methodology

Figure 1 gives the detailed working of the above mentioned method.

The following framework in Fig. 1 explains the functioning of the method that is proposed. The soil samples are initially collected from various fields and the necessary sensors are placed in the soil and the parameters like N, P, K, pH, temperature and humidity are measured in order to predict the crop that is suitable for the type of soil identified. The N, P, K, pH, humidity and temperature sensors are integrated with ADC pin (pin 28, 29, 30, 25, 4, 5) of the micro controller. Once when the data are acquired, they should be converted into binary form so in that case a ADC converter is used and using UART1 the readings are displayed on PC. In order to display the acquired data in the excel format a python code is written and the acquired excel sheet is used for the training purpose. The pre-processing and labelling of the data acquired are done in the diagnostic phase. The prognostic phase is the phase where in the labels that are classified are trained using different ML algorithms and based on the metrics analysed for the algorithm the efficient one is selected. The result also shows the suggestion on how to overcome the deficiency.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Proposed prognostics framework</figcaption></figure>

## 3.1 Block Diagram

The Fig. 2 depicts the block diagram of proposed model. Parameters like Nitrogen(N), phosphorous(P), potassium(K), pH and temperature are analyzed for each soil type in order to predict the crop that can be grown. Degradation in any of the soil parameters may
affect the growth of the plant and in order to identify the suitable range of parameters, they are monitored using the nitrogen, phosphorous, potassium, pH and temperature sensors are attached to LPC2148 micro controller. The temperature sensor used in LM395, the sensor is provided with 4 V to 30 V power supply. It works on the principle that the voltage change which is measured is directly proportional to the temperature of the soil measured. The sensors which are used in the measurement of NPK values are analog and the phenomenon of electrical conductivity is used in determining the content of NPK. Here the electrodes are attached to the soil and electricity is passed to the electrode from which the conductivity according to the different soil parameters can be obtained and the minimum and maximum value for each parameter is programmed and the measurements are obtained. The pH sensor is also a electrode where the principle that exchange of ions in the soil generates the voltage and that analog voltage difference reflects the readable pH value of the soil. LPC2148 is used in the data acquisition process in which the appropriate soil parameters are acquired from the appropriate sensors attached. The data sets are acquired and loaded to the ML algorithms for training the model. The data that are acquired from the real time using the sensors are tabulated in the excel format using the USB to TTL UART serial convertor module which is connected between the ARM and PC. The data sets that are recorded in the excel format is used as the input for the training ML algorithms like Decision tree, GNB, KNN, SVM, logistic regression and random forest. In order to identify the best algorithm that can be used as the classifier, performance parameters of the algorithm like accuracy, precision, F1 score and recall are taken into account.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Block diagram of crop prediction model</figcaption></figure>

# 4 Machine Learning Algorithms

The below mentioned are the predictive analysis algorithms. Various algorithms like k-Nearest neighbour, SVM, GNB, Logistic regression and Decision tree are explained below [11].

## 4.1 Support Vector Machine (SVM)

Both classification and regression problems are solved using SVM algorithm. In SVM in order to create the separation between the different classes a hyper plan is created. And
for the error reduction these hyperplanes are repeated. The main theme of the Support vector machine algorithm is to create a maximal margin hyperplane which are used in dividing input data sets into classes. In SVM using some fixed mapping the data input (x) is converted into a n-dimensional space and the linear models thus obtained are mapped in the n-dimensional spaces The linear model can be represented as

$$ (x, w) = \sum_{j=1}^{n} w_j g_j(x) + c \quad (1) $$

(x), l = 1, ..., n-set of transformations that are nonlinear.

c- Represents the term bias

### Random Forest (RF)

Random Forest is a supervised predictive analysis technique. Ensemble learning is the concept used in random forest algorithm where this process combines multiple number of classifiers to bring solution to the problem for enhancing the model's performance and also to solve complex problems. N-number of decision trees are present in RF and they are dependent on numerous subcategories of data sets given and average value is taken by the algorithm for improvising the precision of the prognosis of the datasets provided. The RF regression model is expressed as

$$ h(x) = g_0(x) + g_1(x) + g_2(x) + \dots \quad (2) $$

Model h – base model gi's aggregation is represented.

### K-Nearest Neighbour (KNN)

K-NN is a supervised predictive analysis algorithm. Both the available and new cases are considered and input data are classified according to the similar available category. KNN are algorithms which are distribution free ie these algorithms does not make any decisions which are dependent on the data sets that are underlying. The Eucledian distance is calculated and based on that the neighbour nodes are laid out in ascendingly. The Eucledian distance is calculated using following formula

$$ d(x, y) = \sum_{i=1}^{n} (X_i - Y_i) \quad (3) $$

### Gaussian Naïve Bayes

GNB is a supervised ML algorithm. Classification related problems can be effectively solved using this algorithm. Text classification is one of the important application of this algorithm. These algorithms are useful in building fast ML based models. GNB is a algorithm that is used in classification and it is also probability based, in which object's probability is used for prediction. The equation denoting the GNB is given as:

$$ P\left(\frac{b}{x}\right) = \frac{P\left(\frac{x}{b}\right)P(b)}{P(b)} \quad (4) $$

P(blx) – it indicates the posterior class probability P(b) - indicates the specific class prob; P(xlb) represents the likelihood.

### Decision Tree

Decision Trees is one of the types of Supervised predictive analysis algorithm where
in the data are regularly split depending on certain parameters. The tree basically has 2 entities, they are the decision nodes and the leaves. The final outcomes are considered to be the leaves and using the decision nodes the data are categorized. The equation for finding entropy in decision tree is

$$E(S) = \sum -pi \log 2 pi \quad (5)$$

## Logistic Regression

Logistic Regression is a supervised predictive analysis algorithms where the data are provided for training is labelled and the algorithm learn from their corresponding labelled values in the training data seta and they use them to classify the new data.

# 5 Experimentation

The Fig. 3 and Fig. 4 depicts the experimental setup for the predictive analysis of crops. Parameters like Nitrogen, Phosphorous, potassium, pH and humidity are obtained through sensor from the soil. These parameters have to be monitored and depending on the analysis of these parameters the suitable crop for the cultivation is predicted. This can also be used in the soil health management. The parameters like N, P, K, pH and humidity are continuously monitored using the appropriate sensor and those sensors are interfaced with the microcontroller for the data acquisition process.LPC2148 is selected as the microcontroller for the acquisition of data. The data that are obtained from LPC2148 are used for training purpose and they are uploaded in excel sheet with the help of CPC2102 USB to TTL UART Serial convertor Module and they are connected between microcontroller and PC. The data sets that are obtained from the LPC2148 are then fed into the ML algorithms like SVM, KNN, Random forest, Decision tree, Logistic regression.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Experimental setup for the proposed system</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Model setup in field</figcaption></figure>
# 6 Results and Analysis

The data collected in real time which are obtained through the experimental setup are given as input to the machine learning algorithms and trained and the conclusions that are obtained are based on the performance parameters of the algorithm like F1 score, accuracy, precision and recall. From the Fig. 5 the result of the random forest algorithm are depicted. The parameters of the algorithm like accuracy, precision, F1 score are obtained. It can be inferred that appreciable number of labels are correctly classified against their respective feature. Here, the rate of accuracy is around 98.6% and the confusion matrix depicts that among 75 labels that are marked as test labels, 65 are predicted correctly, which indicates the true positive count.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Results of RF algorithm</figcaption></figure>

Figure 6. Represents the conclusions obtained for SVM algorithm. The SVM algorithm has the accuracy of 13.3% and from the confusion matrix it is found that among 75 labels that are marked as test labels, 10 are predicted correctly.

Figure 7 represents the conclusions obtained for Naïve bayes algorithm. The Naïve bayes algorithm has the accuracy of 97.3% and from the confusion matrix it is found that among 75 labels that are marked as test labels, 73 are predicted correctly.

Figure 8 represents the conclusions obtained for KNN algorithm. The K-nearest neighbour has the accuracy of 96% and from the confusion matrix it is found that among 75 labels that are marked as test labels, 72 are predicted correctly.
<figure><img src="image_8.png" /><figcaption>Fig. 6. Results of SVM algorithm</figcaption></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. Results of Naïve bayes algorithm.</figcaption></figure>

<figure><img src="image_11.png" /></figure>

Figure 9 represents the conclusions obtained for logistic regression algorithm. The Logistic regression algorithm has the accuracy of 92% and from the confusion matrix it is found that among 75 labels that are marked as test labels, 69 are predicted correctly.

Figure 10 represents the conclusions obtained for Decision tree algorithm. The decision tree algorithm has the accuracy of 86% and from the confusion matrix it is found that among 75 labels that are marked as test labels, 65 are predicted correctly.
<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Results of KNN algorithm</figcaption></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 9.** Results of logistic regression algorithm</figcaption></figure>

<figure><img src="image_15.png" /></figure>

## Results For Crop Prediction and Nutrient Level Analysis

Figure 11 depicts the results for prediction of the crop based on the observed N, P, K, pH, humidity and temperature parameters and also about the nutrient level analysis.

## Analysis of Data Sets

Table 1 shows a relative analysis on metrics of the Machine learning algorithms. Real time data sets are collected and the parameters are analysed. From the below tabulation it is evident that Random forest and Gaussian Naïve Bayes algorithms has the highest performance metrics and they are used in the analysis.

## Conclusion Obtained from the Data Set

Table 2 depicts the specific levels of N, P, K, pH, humidity and temperature of different types of crops which are analysed. These parameters are very useful in crop prediction.
<figure><img src="image_16.png" /><figcaption>Fig. 10. Results of Decision tree algorithm</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 11. Results for crop prediction and nutrient level analysis</figcaption></figure>

Based on the above table the conclusions are made in determining which crop can be grown at that particular parameters.

## Conclusion Obtained for N, P, K Analysis

Table 3 Provides the information about the specific values of NPK which are required for each crop and the analysis is also made in such a way that when the soil has less NPK content the suggestions are given on how to improve the deficient content. This helps the farmers and soil analyst to take an immediate action depending upon the results.
<table><caption>**Table 1.** Relative study on metrics of the algorithms</caption><thead><tr><th>ALGORITHM</th><th>ACCURACY (%)</th><th>PRECISION (%)</th><th>F1-SCORE (%)</th><th>RECALL (%)</th></tr></thead><tbody><tr><td>DECISION TREE</td><td>86</td><td>89</td><td>90</td><td>87</td></tr><tr><td><strong>GAUSSIAN<br>NAÏVE BAYES</strong></td><td><strong>97.3</strong></td><td><strong>97</strong></td><td><strong>98</strong></td><td><strong>97</strong></td></tr><tr><td>SUPPORT<br>VECTOR<br>MACHINE</td><td>13</td><td>02</td><td>14</td><td>03</td></tr><tr><td>LOGISTIC<br>REGRESSION</td><td>92</td><td>93</td><td>92</td><td>92</td></tr><tr><td><strong>RANDOM<br>FOREST</strong></td><td><strong>98.6</strong></td><td><strong>98</strong></td><td><strong>98</strong></td><td><strong>99</strong></td></tr><tr><td>K-NEAREST<br>NEIGHBOUR</td><td>97</td><td>92</td><td>92</td><td>91</td></tr></tbody></table>

<table><caption>**Table 2.** Conclusion obtained from the analysis of data sets</caption><thead><tr><th>N (Kg/ha)</th><th>P (Kg/ha)</th><th>K (Kg/ha)</th><th>pH (%)</th><th>Humidity</th><th>Temperature (°c)</th><th>Crop identified</th></tr></thead><tbody><tr><td>130–150</td><td>40–50</td><td>40–60</td><td>6–6.7</td><td>60–80</td><td>21–37</td><td>Rice</td></tr><tr><td>150–160</td><td>75–90</td><td>30–45</td><td>6.5–7.5</td><td>50–60</td><td>31–32</td><td>Banana</td></tr><tr><td>100–120</td><td>40–65</td><td>50–60</td><td>5.8–8.0</td><td>43–50</td><td>20–32</td><td>Cotton</td></tr><tr><td>150–180</td><td>65–80</td><td>45–60</td><td>5.8–6.2</td><td>55–65</td><td>32</td><td>Corn</td></tr><tr><td>145–150</td><td>70–80</td><td>70–80</td><td>6.8–7.5</td><td>45–60</td><td>20–26</td><td>Sugar Cane</td></tr><tr><td>130–140</td><td>30–40</td><td>25–30</td><td>6.5–6.8</td><td>23–41</td><td>21–24</td><td>Coffee</td></tr><tr><td>70–90</td><td>20–40</td><td>40–45</td><td>5.5–7</td><td>45–53</td><td>23–26.7</td><td>Mango</td></tr></tbody></table>

<table><caption>**Table 3.** Conclusion obtained from N, P, K analysis</caption><thead><tr><th>CROP NAME</th><th>N CONTENT</th><th>P CONTENT</th><th>K CONTENT</th><th>REMARK</th></tr></thead><tbody><tr><td>RICE</td><td>≤125</td><td>≤35</td><td>≤35</td><td>N-adding urea</td></tr><tr><td>BANANA</td><td>≤145</td><td>≤60</td><td>≤30</td><td>P-adding kcl and potassium sulphate</td></tr><tr><td>CORN</td><td>≤140</td><td>≤55</td><td>≤45</td><td>K-adding potassium silicate or ammonium phosphate</td></tr><tr><td>MANGO</td><td>≤68</td><td>≤20</td><td>≤39</td><td></td></tr><tr><td>COFFEE</td><td>≤128</td><td>≤25</td><td>≤25</td><td></td></tr><tr><td>SUGAR CANE</td><td>≤130</td><td>≤69</td><td>≤65</td><td></td></tr></tbody></table>
# 7 Conclusion

The above presented work has the crop prediction method that is dependent on the soil and atmospheric parameters that are measured in real time. The proposed paper is cost efficient and the approximate cost of the project was around 7000. The proposed work also indicates the NPK analysis for the crops and gives the suggestion based on the decrease in any one of the contents. Here various machine algorithms like KNN, GNB, RF, Logistic Regression, SVM and Decision tree are applied to establish the relationship between the soil, environmental parameters and the various types of crops. The accuracy of each proposed ML models are analysed and the model with the higher accuracy is taken for the prediction of the crops based on these parameters.

# References

1. Paul, M., Vishwakarma, S.K.: Analysis of soil behaviour and prediction of crop yield using data mining approach. IEEE Access **6**(1), 65–71 (2019)

2. Bang, S., Bishnoi, R.: Fuzzy logic based crop yield prediction using temperature and rainfall parameters predicted through ARMA, SARIMA, and ARMAX models. IEEE Access **8**(4), 75–80 (2019)

3. Vaishnavi, P., Kalimuthu, M.: Crop prediction using machine learning algorithms. Int. J. Adv. Eng. Technol. **1**(1), 2, 1–8 (2020)

4. Kumar, A., Kumar, N., Vats, V.: Efficient crop yield prediction using machine learning algorithms. Int. Res. J. Eng. Technol. **5**(06) (2019)

5. Dhivya Elavarasan, P.M., Vincent, D.: Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access **8**, 86886–86901 (2020). https://doi.org/10.1109/ACCESS.2020.2992480

6. Keerthana, M.: An ensemble algorithm for crop yield prediction. In: Third International conference on Intelligent communication technologies and virtual mobile network. IEEE Xplore Part Number: CFP21ONG (2020)

7. Khaki, S., Wang, L.: Crop yield prediction using deep neural networks. Front. Plant Sci. **10**, 621 (2019)

8. Kumar, Y.J.N., Spandana, V., Vaishnavi, V.S., Neha, K., Devi, V.G.R.R.: Supervised machine learning approach for crop yield prediction in agriculture sector. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), vol. 4, pp. 736–741. IEEE (2020)

9. Islam, T., Chisty, T.A., Chakrabarty, A.: A deep neural network approach for crop selection and yield prediction in Bangladesh. In: IEEE Region 10 Humanitarian Technology Conference (R10- HTC), vol. 1, pp. 1–6 (2020)

10. Nigam, A., Garg, S., Agrawal, A., Agrawal, P.: Crop yield prediction using machine learning algorithms. In: Fifth International Conference on Image Processing (ICIIP), IEEE Xplore Part Number: CFP21K74-ART (2019)

11. Paul, M., Vishwakarma, S.K., Verma, A.: Analysis of Soil Behavior and Prediction of Crop Yield using Data Mining approach, vol. 10, pp. 65–9 (2019)

12. Malik, P., Sengupta, S.: Comparative analysis of soil properties to predict fertility and crop yield using machine learning algorithms. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (2021)

13. Sujatha, R., Isakki, P.: A study on crop yield forecasting using classification techniques. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE 2016). IEEE (2019)
14. Kale, Shivani S., Patil, P.S.: A machine learning approach to predict crop yield and success rate. In: 2019 IEEE Pune Section International Conference (PuneCon). IEEE (2019)

15. Ramesh, M., Rajpurohit, V.S., Shweta, S.: Crop yield prediction using machine learning techniques. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE, Land, vol. 10, no. 12, pp. 1389 (2021)

16. Dhaya Sree, R., Jayanthy, S., Essaki Vigneshwaran, E.: Estimation of Remaining Useful Life (RUL) of BLDC motor using machine learning approaches. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES) (2022)