AI for sustainability:-Spice Yield Prediction using Indian ...

AI for sustainability:-Spice Yield Prediction using Indian Climatic

Parameters.Dr Nupur Giri

HOD,Dept.Of ComputerEngineering

VESITMumbai, India

[email protected]

Mr Siddarth BugtaniDept. Of Computer

EngineeringVESIT

Mumbai, India2018.siddarth.bugtani@v

es.ac.in

Ms Yashvi HiranandaniDept. Of Computer

EngineeringVESIT

Mumbai, India2018.yashvi.hiranandani

@ves.ac.in

Mr Vishwesh JagtapDept. Of Computer

EngineeringVESIT

Mumbai, India2018.vishwesh.jagtap@ve

s.ac.in

Mr Kabir RajpalDept. Of Computer

EngineeringVESIT

Mumbai, [email protected].

in

Abstract--India is a land of spices and theirsuccessful husbandry depends upon the areaprovided for cultivation and the climaticconditions. India is one of the chief exporters ofspices. Therefore yield forecasting plays a vitalrole in export planning and policy decisions.Overthe years research and development in machinelearning has provided a significant contribution toindustries. This project aims to create a model topredict the yield of spices based on the region ofcultivation and various other input parameterswhich would help the farmers to foresee the yieldand take appropriate measures for storage andplantation of these crops. We have also providedthe prices forecast of the spices to determinewhether it is worth investing in its plantation forthe current year or not. Algorithms such asRandom Forest Regressor, Stochastic GradientDescent and KNN were applied to the datasetafter performing preprocessing steps. Advancedregression techniques like Kernel Ridge, Lassoand ENet algorithms have been used to predictthe yield along with Stacking Regression which

gives us better results in yield prediction. Theresults have been expressed using the meanabsolute error and the r2_score.

Fig 1. Export % of each spice in India

Keywords:-Kernel Ridge Regressor, WholesalePrice Index(WPI) ,Climate,Soil moisture.

I. Introduction:-Farming is the spine for each nation's economyprimarily like India, which has consistently risinginterest for nourishment because of rising populace,

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and in house demand. Agriculture is not only theprimary occupation of our nation, but has also beenthe backbone for the economy since ages. Productionand cultivation play an important role in it. At timesthe productivity of crops is sufficient but the export islagging due to several conditions. Thus the demandfor food keeps on increasing. Spices constitute amajor place in the kitchen as they bring out theunique natural taste of cuisines and could be used tochange the look of food to make it more attractiveand pleasing to the eyes .Along with its variousadvantages, spices have several health benefits.Sinceyears, spices add up to the taste and make thehome-cooked food taste more better, no only in indiabut all around the world. Spices have a lot ofnutritional value thus providing some major healthbenefits.The Economic times[1] pointed out that,“During this covid-19 pandemic, out of India’s totalcrop production, 90% produce was of spices with thetotal export worth 250 billion rupees in one year.” Asper the financial express[2] The cost of export ofvarious spices increased in the first six months of2021 as compared to the past one year..Though Invalue terms, as stated in The Business Today[3],“India exported spices worth Rs 15,882.” Thoughthere is a 8 percent growth as compared to last year,the production and export difference is still notable.The main reason for this difference is the lack ofknowledge. Mainly farmers don’t know where theproduce will be more, they have been practisingtraditional things for over years now. Researchers aretrying their best to bridge this gap. Hence in order toobtain finest results in predicting the crop yield, someof the machine learning algorithms can beimplemented. Machine learning is basically thebranch of computer engineering where the machine istrained to work as a human brain and thus give greatresults, which in turn maximizes the profits andincreases the success. With machine learning, thecomputer is made to do the things that the humandoes. Crop production is a complicated developmentthat's influenced by soil and environmental conditioninput parameters and let alone the traditionaltechniques don’t seem to be helping enough so withthe help of these machine learning methodologies thetask can be done greatly.Though agriculture inputparameters vary from field to field and farmer tofarmer, proper planning, learning, communication,reasoning, knowledge and perception have made this

possible. Modern technological advancement in thefield of yield prediction also helps the farmers withfuture cost prediction. While predicting yield ofcrops, namely spices, we do classification along withprediction. This prediction could be price predictionor prediction of crops suitable in a particular area. Inthis paper we have taken 9 spices grown all aroundIndia and predicted their yield and future prices fromprevious available data as well as variousenvironmental data.The 9 spices which we chose onthe basis of productivity and usage are as followsBlack pepper, cumin, rapeseed and mustard,coriander, turmeric, ginger, garlic, dry chilli andcardamom.Black pepper since India is one of itsmajor producers. Ginger and garlic hold a hugeposition in the urban market. The reason behindchoosing Cardamom is that as per FBNews.com[4] itis the third most expensive spice when seen globally,whereas turmeric is the commonly used Indian spice.The Mordor Intelligence[5] states that, “19% of therural market is dominated by dry chillies.” Corianderbeing the oldest spice and cumin being dense in iron,helps in iron deficiency. With our system, the farmerswill be handed in with all the necessary informationspecific to a region. Right from the best crop for thatregion to the future prices. Thus from this yieldprediction, we provide the necessary information tothe farmers thus helping increase their yield and alsoletting them know about the types of crops to becultivated during that climate.

II. Related Work:-

1. SML Venkata Narasimhamurthy & et al [6].“Rice Crop Yield Forecasting UsingRandom Forest Algorithm”. In this paper,this algorithm is used to predict the yield ofrice, by considering the climatic conditionsas the parameters thus helping farmers inmaking decision.Climatic conditions likerainfall,temperature and data like perceptionand rice production in tonnes have beentaken into consideration.Maximum accuracyof 85. 89% is obtained by using this method.

2. P. Priya*1 & et al [7] . “Predicting the CropYield Using Machine Learning Algorithm”.In this paper, a Random forest algorithm hasbeen used to check the yield of the crop as



per the hectares, before farming onto thefield. Data sets such as rainfall, perception,temperature, production is taken intoaccount. Decision tree classifier has beenused. Here, accuracy can be predicted byrelating the resultant class value with the testdata set.

3. (Filippi et al., 2019)[8] proposed randomforest models combined with STC to predictwheat, barley, canola yield for threedifferent seasons. LOFOCV and LOFYOCcross validation methods are used tomeasure the prediction quality of themodels. The RMSE of the models suggestthat more data driven models can bedeveloped using machine learningalgorithms to predict crop yield.

4. (Tamsekar et al., 2019)[9] proposed a cropselection prediction model using GIS data,irrigation data, chemical data, soil data andyield data. Machine learning models such asCART, KNN, RF and SVM are used to build

the prediction model. PCA is used toimprove the model through dimensionreduction. The SVM with PCA showedbetter results with an accuracy of 77%.

5. Saranya C P, Guru Murthy B [10] "ASURVEY ON CROP YIELD PREDICTIONUSING MACHINE LEARNINGALGORITHMS". This paper focuses on aconcise relative work of various papers thatdeals with different techniques used toevaluate the crop yield. It aims at predictingcrop yield (Wheat) by considering andexamining the datasets of previous years ofthe crop. In addition, this paper presentsvarious existing techniques to audit cropyield. It also constitutes the contrast ofvarious algorithms along with its benefitsand drawbacks. The algorithms discussedare Random Forest,Linear Support VectorMachine,Support Vector Machine ,k-Nearest Neighbour,Artificial NeuralNetworks , Decision Tree , Multiple LinearRegression.

III. Materials and Methods:-

A. Datasets:-1. Crop Data: Obtained from data.gov.in.The

columns in the dataset areState,District,Crop,Year, Season,Area andProduction.

2. Climatic Data: The dataset contains valuesof temperature and humidity of states from1989 to 2015.

3. Soil Data: This dataset contains soilmoisture and soil ph level values of theStates.

4. Rainfall Data: Rainfall data was collectedfrom Indian Water Portal. Consists of datafor every month from 2000 to 2018.

5. WPI values: Obtained from data.gov.in,thisdataset has WPI values for most of the cropsincluding a few spices.

B. Data Mapping:-The project consists of 2 sections.For the first sectionCrop , Climate , Rainfall and Soil data were mergedand the spices were segregated from the mergeddataset.For the second part,WPI values of spices are used.These values are combined with the Rainfall dataset.

C. Data pre-processing:-For initial analysis purposes Standardization offeature values was performed using Standard Scalerand categorical features were label encoded usingLabel Encoder before using data for training themodel.



IV. Methodology:-

Fig 2. Modular Diagram for the proposed system

A. Understanding correlation ofclimatic factors on the spicesyield

A correlation matrix is a table showingcorrelation coefficients betweenvariables. Each cell in the table shows thecorrelation between two variables. Acorrelation matrix is used to summarizedata, as an input into a more advancedanalysis, and as a diagnostic foradvanced analyses.According to Fig 3, all the climaticfactors such as Rainfall,Temperature,AirHumidity as well as the soil parameterssuch as Soil moisture and Soil pH levelwere taken into consideration.

Temperature has a good correlation withCrop Year and Area.Humidity has a goodcorrelation with Area andTemperature.Soil moisture has a goodcorrelation with Year,Area andTemperature.This detailed analysishelped us to predict the climatic factorsfor the forthcoming years.

The final correlation plot i.e. Fig 4 hasthe climatic factors which have a goodcorrelation with the production.Thefactors are Temperature,Humidity andSoil moisture.

Fig 3.Correlation matrix for all climatic factors

Fig 4. Correlation plot with selected climaticfactors



B. Prediction Model for Yield

The dataset is split into 75% train data and 25%test data.The algorithms used were LinearRegressor, Random Forest Regressor, StochasticGradient Descent (SGD) Regressor and KNN.These algorithms were applied on scaled as wellas non scaled data. On plotting box plots as in Fig5. and Fig 6. , the presence of outliers was noticedwhich reduced the accuracy of the abovementioned algorithms. Since the area provided forcultivation varied from State to State theproduction also varied drastically. Training themodel on the entire dataset did not provideaccurate results. Therefore

we decided to train the model based on the userinput.The user input includes the parameters:-

● State● District● Area● Crop● Year● Season

The dataset would be segregated according to theabove parameters provided by the user.The modelwould receive the training data as per the aboveparameters inputted by the user.The climaticfactors for the desired year will be predicted andfed to the model.

Fig 5. Production outliers Fig 6. Area outliers

V. RESULT:-This paper demonstrates the use of variousregression algorithms Random ForestRegressor,Stochastic Gradient Descent(SGD),Linear Regression , Keras , KNN for predictingspice yield in a region.A comparative study wasperformed using five different regressionalgorithms to enhance accuracy.The accuracies of

different models were compared after building themodels.Random Forest Classifier Algorithm topsthe list of the lowest mean absolute error. Fig 7shows the bar graph representation of meanabsolute score of the different algorithms used toanalyze the yield.

Fig 7. Mean absolute error bar graph

Algorithm used CropYieldPredictionr2 score

Comments

Random ForestRegressor

0.6 Outliers reducing accuracy

StochasticGradient Descent

Regressor

0.237 Overfitting

KernelRidgeRegressor

0.8 Handles the outliers and workswell on medium sized datasets.

KNN 0.238 Sensitive to scale of data andirrelevant features

Fig 8. R2_score comparison



WPI TREND FROM JUN ‘21 TO MAY ‘22:-

Fig 9. Black Pepper Fig 10. Turmeric

Fig 11. Dry Chilly Fig 12. Cumin

Fig 13.Yield Prediction of Coriander



● Fig 13 shows the prediction of Production of Coriander for the state Bihar,district wise using KernelRidge

● For the WPI section ,the model predicts WPI values from June 2021 to May 2022. Fig 9 - 12,show thevariation of predicted WPI values for the crops Black Pepper,Turmeric,Cumin and Chilly. The redconstant line in the above plots indicate current prices of the specified spices.

VI. FUTURE SCOPE:-

1. Use of Weather APIs in order to get real-time climatic data for prediction purposes.2. Creating a system wherein the farmer can Login as well as Register and also see frequently asked

questions.3. Embedding a chatbot for farmers in multiple languages.

VII. CONCLUSION:-

The proposed framework gives a brief idea about the spices which are most productive in a particular regionbased on the input parameters of the user. As the framework drills down every conceivable harvest, it helps thecultivator in the dynamic of which yield to develop at which season. Later IOT devices can also be used with thismodel in order to receive accurate data like soil pH levels, soil moisture, temperature and humidity. The variousalgorithms used give us an insight into the features which are most useful in predicting the yield. The futureendeavour would be to include more attributes which would help in improving the yield results.

VIII. REFERENCES:-[1] Rituraj Tiwari, ET Bureau, “India's farmexports rise 23.24% in March-June 2020 despitepandemic” ET Prime ,Aug 18, 2020. [Online],Available: https://economictimes.indiatimes.com

[2] FE Bureau,”COVID-19 impact: Indianspices rise in demand for immunity properties,exports up 34% in June”,July 18, 2020,[Online],Available:https://www.businesstoday.in/

[3] PB Jayakumar, “India’s spices exports grow19% during the first half of FY21”, December 23, 2020 .[Online], Available:https://www.financialexpress.com

[4] B Sasikumar, “Cardamom - The third mostexpensive spice in the world”,FBNews, 17 August,2015[Online], Available: http://www.fnbnews.com

[5] Mordor Intelligence, “DRY CHILLIESMARKET - GROWTH, TRENDS, COVID-19IMPACT, AND FORECASTS (2021 - 2026)”, 2021.[Online]. Available:https://www.mordorintelligence.com/industry-reports/dry-chillies-market

[6] SML Venkata Narasimhamurthy, AVS

pavan Kumar, “Rice crop yield forecasting usingrandom forest algorithm”, International journal forresearch in Applied science and Engineeringtechnology, vol.5, Issue 10, october 2017,Pp:1220- 1225

[7] P.Priya, U.Muthaiah, M.Balamurugan,“Predicting yield of the crop using machinelearning Algorithm”, IJESRT et al., 7(4):April-2018, pp 2277-2284

[8] Filippi, P., Jones, E. J., Wimalathunge, N.S., Somarathna, P. D., Pozza, L. E., Ugbaje, S. U.,... & Bishop, T. F. (2019). An approach to forecastgrain crop yield using multi-layered, multifarmdata sets and machine learning. PrecisionAgriculture, 1- 15.

[9] Tamsekar, P., Deshmukh, N.,Bhalchandra, P., Kulkarni, G., Hambarde, K., &Husen, S. (2019). Comparative Analysis ofSupervised Machine Learning Algorithms forGIS-Based Crop Selection Prediction Model. InComputing and Network Sustainability (pp.309-314)

[10] Saranya C P , Guru Murthy B,Karuppasamy M, Sunmathi M , Shree SakthiKeerthna S (2020) “A SURVEY ON CROPYIELD PREDICTION USING MACHINELEARNING ALGORITHMS”.



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