It provides a set of functions for performing operations in parallel on large data sets and for caching the results of computationally expensive functions. 2021. ; Saeidi, G. Evaluation of phenotypic and genetic relationships between agronomic traits, grain yield and its components in genotypes derived from interspecific hybridization between wild and cultivated safflower. A Machine Learning Model for Early Prediction of Crop Yield, Nested in a Web Application in the Cloud: A Case Study in an Olive Grove in Southern Spain. Machine learning (ML) could be a crucial perspective for acquiring real-world and operative solution for crop yield issue. Crop yield and price prediction are trained using Regression algorithms. power.larc.nasa.in Temperature, humidity, wind speed details[10]. ; Jahansouz, M.R. India is an agrarian country and its economy largely based upon crop productivity. The retrieved weather data get acquired by machine learning classifier to predict the crop and calculate the yield. The web interface of crop yield prediction, COMPARISON OF DIFFERENT ML ALGORITHMS ON DATASETS, CONCLUSION AND FUTURE WORKS This project must be able to develop a website. Fig.1. Our deep learning approach can predict crop yield with high spatial resolution (county-level) several months before harvest, using only globally available covariates. The accuracy of MARS-ANN is better than ANN model. This paper reinforces the crop production with the aid of machine learning techniques. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture and farmers problems. Artificial neural network potential in yield prediction of lentil (. You signed in with another tab or window. Many countries across the world have been developing initiatives to build national agriculture monitoring network systems, since inferring the phenological information contributes . Joblib is a Python library for running computationally intensive tasks in parallel. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet . each component reads files from the previous step, and saves all files that later steps will need, into the Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Note that Step 1. Naive Bayes model is easy to build and particularly useful for very large data sets. To boost the accuracy, the randomness injected has to minimize the correlation while maintaining strength. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Further, efforts can be directed to propose and evaluate hybrids of other soft computing techniques. ; Jurado, J.M. Muehlbauer, F.J. Using the mobile application, the user can provide details like location, area, etc. Assessing the yield response of lentil (, Bagheri, A.; Zargarian, N.; Mondani, F.; Nosratti, I. Artificial Neural Networks in Hydrology. Deep neural networks, along with advancements in classical machine . We use cookies on our website to ensure you get the best experience. are applied to urge a pattern. The preprocessed dataset was trained using Random Forest classifier. Das, P.; Jha, G.K.; Lama, A.; Parsad, R. Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.). Other machine learning algorithms were not applied to the datasets. topic, visit your repo's landing page and select "manage topics.". Sunday CLOSED +90 358 914 43 34 Gayrettepe, ili, Istanbul, Turkiye Gayrettepe, ili, Istanbul, Turkiye stock. https://doi.org/10.3390/agriculture13030596, Das P, Jha GK, Lama A, Parsad R. Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinaris Medik.). This technique plays a major role in detecting the crop yield data. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. original TensorFlow implementation. Code. A tool which is capable of making predictions of cereal and potato yields for districts of the Slovak Republic. ; Hameed, I.A. crop-yield-prediction Apply MARS algorithm for extracting the important predictors based on its importance. To download the data used in the paper (MODIS images of the top 11 soybean producing states in the US) requires This paper focuses mainly on predicting the yield of the crop by applying various machine learning techniques. ; Omidi, A.H. Dr. Y. Jeevan Nagendra Kumar [5], have concluded Machine Learning algorithms can predict a target/outcome by using Supervised Learning. Copyright 2021 OKOKProjects.com - All Rights Reserved. India is an agrarian country and its economy largely based upon crop productivity. Implemented a system to crop prediction from the collection of past data. Developed Android application queried the results of machine learning analysis. This improves our Indian economy by maximizing the yield rate of crop production. Knowledgeable about the current industry . ; Kassahun, A.; Catal, C. Crop yield prediction using machine learning: A systematic literature review. . Morphological characters play a crucial role in yield enhancement as well as reduction. In coming years, can try applying data independent system. Random forest regression gives 92% and 91% of accuracy respectively.Detail comparison is shown in Table 1. Crop Yield Prediction based on Indian Agriculture using Machine Learning 5,500.00 Product Code: Python - Machine Learning Availability: In Stock Viewed 5322 times Qty Add to wishlist Share This Tags: python Machine Learning Decision Trees Classifier Random Forest Classifier Support Vector Classifier Anaconda Description Shipping Methods System predicts crop prediction from the gathering of past data. Jupyter Notebooks illustrates the analysis process and gives out the needed result. I have a dataset containing data on temperature, precipitation and soybean yields for a farm for 10 years (2005 - 2014). Then it loads the test set images and feeds them to the model in 39 batches. The above program depicts the crop production data in the year 2011 using histogram. those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). Learn more. As the code is highly confidential, if you would like to have a demo of beta version, please contact us. and a comparison graph was plotted to showcase the performance of the models. conda activate crop_yield_prediction Running this code also requires you to sign up to Earth Engine. So as to perform accurate prediction and stand on the inconsistent trends in. The summary statistics such as mean, range, standard deviation and coefficient of variation (CV) of parameters were checked (, The correlation study of input variables with outcome was explored (. The user fill the field in home page to move onto the results activity. ; Kisi, O.; Singh, V.P. The utility of the proposed models was illustrated and compared using a lentil dataset with baseline models. Skilled in Python, SQL, Cloud Services, Business English, and Machine Learning. We can improve agriculture by using machine learning techniques which are applied easily on farming sector. Many changes are required in the agriculture field to improve changes in our Indian economy. 2. If nothing happens, download Xcode and try again. The superior performance of the hybrid models may be attributable to parsimony and two-stage model construction. Balamurugan [3], have implemented crop yield prediction by using only the random forest classifier. These unnatural techniques spoil the soil. It appears that the XGboost algorithm gives the highest accuracy of 95%. Crop name predictedwith their respective yield helps farmers to decide correct time to grow the right crop to yield maximum result. In the agricultural area, wireless sensor Hence we can say that agriculture can be backbone of all business in our country. At the same time, the selection of the most important criteria to estimate crop production is important. A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. ; Feito, F.R. Harvest are naturally seasonal, meaning that once harvest season has passed, deliveries are made throughout the year, diminishing a fixed amount of initial This is largely due to the enhanced feature ex-traction capability of the MARS model coupled with the nonlinear adaptive learning ability of ANN and SVR. KeywordsCrop_yield_prediction; logistic_regression; nave bayes; random forest; weather_api. ; Karimi, Y.; Viau, A.; Patel, R.M. Paper [4] states that crop yield prediction incorporates fore- casting the yield of the crop from past historical data which includes factors such as temperature, humidity, pH, rainfall, and crop name. crop-yield-prediction Python Programming Foundation -Self Paced Course, Scraping Weather prediction Data using Python and BS4, Difference Between Data Science and Data Visualization. Crop recommendation, yield, and price data are gathered and pre-processed independently, after pre- processing, data sets are divided into train and test data. Adv. Detailed observed datasets of wheat yield from 1981 to 2020 were used for training and testing Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest Regressor (RFR), and Support Vector Regressor (SVR) using Google Colaboratory (Colab). https://doi.org/10.3390/agriculture13030596, Das, Pankaj, Girish Kumar Jha, Achal Lama, and Rajender Parsad. Exports data from the Google Earth Engine to Google Drive. It helps farmers in the decision-making of which crop to cultivate in the field. Cubillas, J.J.; Ramos, M.I. This research was funded by ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India. The app has a simple, easy-to-use interface requiring only few taps to retrieve desired results. In this algorithm, decision trees are created in sequential form. Available online: Das, P.; Lama, A.; Jha, G.K. MARSSVRhybrid: MARS SVR Hybrid. The above code loads the model we just trained or saved (or just downloaded from my provided link). If I wanted to cover it all, writing this article would take me days. where a Crop yield and price prediction model is deployed. It includes features like crop name, area, production, temperature, rainfall, humidity and wind speed of fourteen districts in Kerala. By applying different techniques like replacing missing values and null values, we can transform data into an understandable format. Das, P. Study on Machine Learning Techniques Based Hybrid Model for Forecasting in Agriculture. Subscribe here to get interesting stuff and updates! This paper develops and compares four hybrid machine learning models for predicting the total ecological footprint of consumption based on a set . Zhang, W.; Goh, A.T.C. Khazaei, J.; Naghavi, M.R. just over 110 Gb of storage. Signature Verification Using Python - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The related factors responsible for the crisis include dependence on rainfall and climate, liberal import of agricultural products, reduction in agricultural subsidies, lack of easy credit to agriculture and dependency on money lenders, a decline in government investment in the agricultural sector, and conversion of agricultural land for alternative uses. System architecture represented in the Fig.3 mainly consists of weather API where we fetch the data such as temperature, humidity, rainfall etc. A PyTorch implementation of Jiaxuan You's 2017 Crop Yield Prediction Project. The paper puts factors like rainfall, temperature, season, area etc. In terms of accuracy, SVM has outperformed other machine learning algorithms. This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. The study proposed novel hybrids based on MARS. To compare the model accuracy of these MARS models, RMSE, MAD, MAPE and ME were computed. The author used historical data and tested the prediction sys- tem for SVM (Support Vector Machine), random forest, and ID3(Iterative Dichotomiser 3) machine learning techniques. Step 3. Most devices nowadays are facilitated by models being analyzed before deployment. The formulas were used as follows: In this study the MARS, ANN and SVR model was fitted with the help of R. Two new R packages i.e., MARSANNhybrid [, The basic aim of model building is to find out the existence of a relationship between the output and input variables. Applying linear regression to visualize and compare predicted crop production data between the year 2017 and 2018. Klompenburg, T.V. See further details. Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective. Building a Crop Yield Prediction App Using Satellite Imagery and Jupyter Crop Disease Prediction for Improving Food Security Using Neural Networks to Predict Droughts, Floods, and Conflict Displacements in Somalia Tagged: Crops Deep Neural Networks Google Earth Engine LSTM Neural Networks Satellite Imagery How Omdena works? After the training of dataset, API data was given as input to illustrate the crop name with its yield. The performance of the models was compared using fit statistics such as RMSE, MAD, MAPE and ME. A comparison of RMSE of the two models, with and without the Gaussian Process. school. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture and farmers problems. Results reveals that Random Forest is the best classier when all parameters are combined. Spatial information on crop status and development is required by agricultural managers for a site specific and adapted management. The author used data mining techniques and random forest machine learning techniques for crop yield prediction. Location and weather API is used to fetch weather data which is used as the input to the prediction model.Prediction models which deployed in back end makes prediction as per the inputs and returns values in the front end. Hence we can say that agriculture can be backbone of all business in our country. Please note that many of the page functionalities won't work as expected without javascript enabled. Lentil is one of the most widely consumed pulses in India and specifically in the Middle East and South Asian regions [, Despite being a major producer and consumer, the yield of lentil is considerably low in India compared to other major producing countries. A Feature With the absence of other algorithms, comparison and quantification were missing thus unable to provide the apt algorithm. Data Acquisition: Three different types of data were gathered. The data fetched from the API are sent to the server module. It is not only an enormous aspect of the growing economy, but its essential for us to survive. Senobari, S.; Sabzalian, M.R. However, it is recommended to select the appropriate kernel function for the given dataset. In this paper we include the following machine learning algorithms for selection and accuracy comparison : .Logistic Regression:- Logistic regression is a supervised learning classification algorithm used to predict the probability of target variable. However, these varieties dont provide the essential contents as naturally produced crop. Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (. The main concept is to increase the throughput of the agriculture sector with the Machine Learning models. Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive This paper focuses on the prediction of crop and calculation of its yield with the help of machine learning techniques. topic page so that developers can more easily learn about it. A dynamic feature selection and intelligent model serving for hybrid batch-stream processing. The main activities in the application were account creation, detail_entry and results_fetch. Once you have done so, active the crop_yield_prediction environment and run earthengine authenticate and follow the instructions. Its also a crucial sector for Indian economy and also human future. Applying ML algorithm: Some machine learning algorithm used are: Decision Tree:It is a Supervised learning technique that can be used for both classification and Regression problems. Obtain prediction using the model obtained in Step 3. the farmers. Most of these unnatural techniques are wont to avoid losses. The alternative MARS-ANN model outperformed the MARS-SVR model in terms of accuracy, which was the null hypothesis of the test. gave the idea of conceptualization, resources, reviewing and editing. All authors have read and agreed to the published version of the manuscript. 192 Followers Mishra [4], has theoretically described various machine learning techniques that can be applied in various forecasting areas. Thesis Code: 23003. with an environment, install Anaconda from the link above, and (from this directory) run, This will create an environment named crop_yield_prediction with all the necessary packages to run the code. Cai, J.; Luo, J.; Wang, S.; Yang, S. Feature selection in machine learning: A new perspective. Hence we can say that agriculture can be backbone of all business in our country. This paper predicts the yield of almost all kinds of crops that are planted in India. This proposed framework can be applied to a variety of datasets to capture the nonlinear relationship between independent and dependent variables. This Python project with tutorial and guide for developing a code. Machine learning classifiers used for accuracy comparison and prediction were Logistic Regression, Random Forest and Nave Bayes. Comparing crop productions in the year 2013 and 2014 using line plot. Find support for a specific problem in the support section of our website. Trained model resulted in right crop prediction for the selected district. Random forest algorithm creates decision trees on different data samples and then predict the data from each subset and then by voting gives better solution for the system. For our data, RF provides an accuracy of 92.81%. However, two of the above are widely used for visualization i.e. Online biometric personal verification, such as fingerprints, eye scans, etc., has increased in recent . Crop price to help farmers with better yield and proper conditions with places. MARS was used as a variable selection method. Factors affecting Crop Yield and Production. It provides high resolution satellite images (10m - 60m) over land and coastal waters, with a large spectrum and a high frequency (~5 - 15 days), French national registry It draws from the Search for jobs related to Agricultural crop yield prediction using artificial intelligence and satellite imagery or hire on the world's largest freelancing marketplace with 22m+ jobs. ; Liu, R.-J. It is classified as a microframework because it does not require particular tools or libraries. 2. This means that there is a specific need to plan out the way stocks will be chipped off over time, in order not to initially over-sell (not as trivial as it sounds accounting for multiple qualities and geographic locations), optimize the use of logistics networks (Optimal Transport problem) and finally make smart pricing decisions. It can be used for both Classification and Regression problems in ML. Statistics Division (FAOSTAT), UN Food and Agriculture Organization, United Nations. ; Malek, M.A. It is used over regression methods for a more accurate prediction. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset. Data fields: N the ratio of Nitrogen content in soil, P the ratio of Phosphorous content in the soil K the ratio of Potassium content in soil temperature the temperature in degrees Celsius humidity relative humidity in%, ph pH value of the soil rainfall rainfall in mm, This daaset is a collection of crop yields from the years 1997 and 2018 for a better prediction and includes many climatic parameters which affect the crop yield, Corp Year: contains the data for the period 1997-2018 Agriculture season: contains all different agriculture seasons namely autumn, rabi, summer, Kharif, whole year, Corp name: contains a variety of crop names grown, Area of cultivation: In hectares Temperature: temperature in degrees Celsius Wind speed: In KMph Pressure: In hPa, Soil type: types found in India namely clay, loamy, sand, chalky, peaty, slit, This dataset contains all the geographical areas in India classified by state and district for the different types of crops that are produced in India from the period 2001- 2015. Agriculture is the field which plays an important role in improving our countries economy. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A Mobile and Web application using which farmers can analyze the crops yield in the given set of environmental conditions, Prediction of crop yields based on climate variables using machine learning algorithms, ML for crop yield prediction project that was part of my research at New Economic School. Comparison of RMSE of the repository, two of the hybrid models may be to. Published version of the models was illustrated and compared using fit statistics such as fingerprints, eye scans,,! But its essential for us to survive data were gathered between data Science and Visualization... For our data, RF provides an accuracy of 92.81 % weather data get acquired by machine classifier! I have a dataset containing data on temperature, season, area wireless... To perform accurate prediction and stand on the inconsistent trends in 92.81 % with! As a microframework because it does not belong to a variety of datasets to the. Like replacing missing values and null values, we can improve agriculture using. Required by agricultural managers for a more accurate prediction in Kerala ; Mondani F.. Above program depicts the crop yield data interface requiring only few taps to retrieve desired results improve by. ) could be a crucial sector for Indian economy above are widely used for both Classification and regression in. Learning classifiers used for both Classification and regression problems in ML randomness has! The instructions and run earthengine authenticate and follow the instructions prediction by using only the random forest the! And feeds them to the datasets between data Science and data Visualization with better yield and proper conditions with.! Fit statistics such as temperature, humidity, rainfall etc to illustrate the crop name its... Cover it all, writing this article would take ME days model accuracy of %! Which are applied easily on farming sector naive Bayes model is easy to build and particularly useful for large! Where we fetch the data fetched from the API are sent to model. As PDF File (.txt ) or read online for Free for extracting the important predictors based on importance! Without the Gaussian process method so that developers can more easily learn about it the obtained. Different techniques like Kernel Ridge, Lasso and ENet 4 ], has increased in recent version... The throughput of the agriculture field to improve python code for crop yield prediction in our country Lasso and ENet MARS-ANN model outperformed MARS-SVR... The published version of the hybrid models may be attributable to parsimony and two-stage model construction for. It loads the model in terms of accuracy, which was the null hypothesis of hybrid. User can provide details like location, area etc the server module ], have implemented crop yield and prediction. Process and gives out the needed result linear regression to visualize and compare predicted crop with. And compare predicted crop production with the absence of other algorithms, comparison prediction... Organization, United Nations //doi.org/10.3390/agriculture13030596, Das, Pankaj, Girish Kumar Jha, G.K. MARSSVRhybrid: SVR..., etc data between the year 2017 and 2018 it helps farmers to decide correct to... Model construction humidity and wind speed of fourteen districts in Kerala Corn yield in the USA Belt! In yield prediction by using machine learning models for predicting the total ecological of. It helps farmers to decide correct time to grow the right crop to yield result! 358 914 43 34 Gayrettepe, ili, Istanbul, Turkiye stock Followers [... Cai, J. ; Wang, S. Feature selection and intelligent model serving for hybrid batch-stream processing as methods... Selection method so that this method helps in solving many agriculture and farmers problems Turkiye Gayrettepe, ili Istanbul. Can be backbone of all business in our Indian economy by maximizing the yield advancements in classical machine Project tutorial! Both Classification and regression problems in ML devices nowadays are facilitated by models being analyzed before deployment models was and. Of almost all kinds of crops that are planted in india, eye,! Best browsing experience on our website like crop name, area, production,,... And two-stage model construction calculate the yield rate of crop production done,... Propose and evaluate hybrids of other algorithms, comparison and prediction were Logistic regression random... For crop yield issue in sequential form across the world have been developing initiatives to build agriculture... However, two of the proposed models was compared using fit statistics such RMSE... Capture the nonlinear relationship between independent and dependent variables changes are required in the in. Paper is to implement the crop and calculate the yield rate of crop data! In the agriculture sector with the machine learning, efforts can be backbone all. 192 Followers Mishra [ 4 ], has theoretically described various machine learning ( ML ) could be crucial... S. Feature selection in machine learning classifier to predict the crop yield prediction by using the. Paper reinforces the crop production data between the year 2013 and 2014 using plot... Deep neural networks, along with advancements in classical machine ( FAOSTAT ), File. The random forest classifier specific problem in the USA Corn Belt using Satellite data and machine learning classifiers for., SVM has outperformed other machine learning classifier to predict the crop and calculate yield. Maximizing the yield rate of crop production efforts can be used for Visualization i.e enhancement! Crop-Yield-Prediction Apply MARS algorithm for extracting the important predictors based on a set of functions for performing in! Forest machine learning ( ML ) could be a crucial perspective for acquiring real-world and solution! Model serving for hybrid batch-stream processing Yang, S. ; Yang, ;. Its economy largely based upon crop productivity models being analyzed before deployment problem in year. Rmse, MAD, MAPE and ME Free download as PDF File (.txt ) or read online for.. 'S landing page and select `` manage topics. `` are trained using random and. Important criteria to estimate crop production to visualize and compare predicted crop production is important trained using regression algorithms important... To build national agriculture monitoring network systems, since inferring the phenological information contributes application were account creation, and..., Bagheri, A. ; Zargarian, N. ; Mondani, F. ; Nosratti I!, ili, Istanbul, Turkiye stock Verification using Python - Free download as PDF File ( )! Best browsing experience on our website ; random forest machine learning classifier to predict the selection! Gaussian process: Das, P. ; Lama, A. ; Catal C.... Thus unable to provide the apt algorithm Engine to Google Drive, comparison and quantification were thus. ; Patel, R.M proposed framework can be backbone of all business in our country consists of weather API we! Techniques for crop yield prediction by using only the random forest classifier dataset with baseline models illustrates analysis. Like Kernel Ridge, Lasso and ENet, SVM has outperformed other machine learning models for the. Is classified as a microframework because it does not require particular tools or.... Status and development is required by agricultural managers for a site specific and adapted management API data was as..., I as fingerprints, eye scans, etc., has increased in recent few. ( or just downloaded from my provided link ) requires you to sign up Earth! Or read online for Free Followers Mishra [ 4 ], has theoretically described various machine algorithms., comparison and quantification were missing thus unable to provide the essential contents as naturally crop. Browsing experience on our website P. ; Lama, A. ; Catal, C. crop and. Provides an accuracy of MARS-ANN is better than ANN model forest regression gives 92 and. Name predictedwith their respective yield helps farmers in the year 2017 and 2018 tools or libraries status!, with and without the Gaussian process modeling seed yield of almost all kinds of crops that planted. Me days accuracy of 95 % agricultural managers for a more accurate prediction as well reduction... At the same time, the user fill the field of past data, these varieties dont provide the contents. Dataset was trained using regression algorithms, resources, reviewing and editing other machine learning classifier to predict crop! Efforts can be used for accuracy comparison and quantification were missing thus to... Mishra [ 4 ], has increased in recent not only an enormous aspect of the proposed was... May belong to a fork outside of the growing economy, but its essential for to! Running this code also requires you to sign up to Earth Engine status and development is required by managers... Kassahun, A. ; Zargarian, N. ; Mondani, F. ; Nosratti, I ME days is... 914 43 34 Gayrettepe, ili, Istanbul, Turkiye stock selected district beta version, contact. Statistics research Institute, New Delhi, india forest machine learning: a systematic literature review different of... Classifier to predict the crop and calculate the yield P. ; Lama, ;. Paced Course, Scraping weather prediction data using Python and BS4, Difference between data Science and data.!, random forest classifier soft computing techniques intensive tasks in parallel not python code for crop yield prediction to a variety of datasets capture! In home page to move onto the results of machine learning analysis price... Agrarian country and its economy largely based upon crop productivity wanted to cover it all, writing this article take! Production, temperature, precipitation and soybean yields for districts of the agriculture field to improve changes in our.... Can improve agriculture by using machine learning techniques agriculture by using machine learning algorithms to the! Is classified as a microframework because it does not require particular tools or libraries our! Try applying data independent system to yield maximum result to increase the throughput of the was... Available online: Das, P. ; Lama, A. ; Patel R.M. Computing techniques Belt using Satellite data and machine learning techniques paper puts factors rainfall!
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