Crop Yield Prediction Using Machine Learning

Through the use of machine learning for crop yield prediction, we can assist the farmers by providing proper guidance for crop farming, resource optimization and also offer food safety. So now a days we handle numerous research wok for crop yielding as serves as a hot topic for upcoming students. A complete research guidance will be provided. Get your Article Writing done by our professional experts in the best way by following your university guidelines. For a specific period of time or season, ML framework helps to predict the crop production by considering several circumstances such as soil nature, farming procedures and weather condition. Below, we described about the procedural flow that we apply for your research work:

  1. Problem Description:

We choose the particular area and suitable crops to forecast the crop production. Based on the selected area and crops, there may be difference in the characteristics and necessary data.

  1. Gathering of Data:

Previous data are collected by us in terms of various types such as:

  • Soil Data: We collect data in terms of pH level, soil nature, electrical conductivity, organic carbon, etc.
  • Weather Data: In this, humidity, temperature, sunshine time-period, rainfall and wind speed are considered by us.
  • Farming Practices: Agriculture procedures, utilized pesticides, fertilizers, planting density are analyzed.
  • Historical Crop Production: For the planned crop, we examine the last year’s production.
  • Remote Sensing Data: We utilize Normalized Difference Vegetation Index (NDVI) from satellite images.
  1. Preprocessing of Data:
  • Managing Missing Data: To manage the missing values, we utilize approaches including regression, imputation or latest techniques such as Multiple Imputation by Chained Equation (MICE).
  • Standardization or Normalization technique: To obtain unit variance and zero mean, we normalize or standardize the data in our work.
  • Feature Engineering: By considering the field knowledge such as growing degree days i.e. an evaluation of heat accumulation, the new features are developed by us.
  1. Selection of Features:

For crop yield forecasting, we selected only the essential features by employing methods like Recursive feature Elimination (RFE) or feature importance methods related to tree based approaches.

  1. Model Selection:

In our project, we utilized various ML frameworks for forecasting crop yield:

  • Tree-based Framework: We can utilize techniques like Random Forest, Decision Trees, Gradient Boosted Trees such as LightGBM and XGBoost.
  • Neural Networks: When working with excessive amount of data or remote sensing images, neural network model can be employed in our research.
  • Regression Framework: Various methods are used by us such as Linear Regression, Ridge, Lasso, etc.
  • Time series framework: When the yield forecasting is based on time series problem, we make use of ARIMA, LSTM and Prophet.
  1. Training of Model:
  • The data are divided in our research into training data, testing data and validation data.
  • We trained our selected framework by using training set and validated by utilizing validation data.
  1. Evaluation:

By using test dataset, we examine our framework’s efficiency in terms of the following metrics:

  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Root Mean Squared Error (RMSE)
  • R-squared value
  1. Optimization:

We carry out the below specified processes by considering effectiveness of our framework:

  • Hyperparameter Tuning: For this, we can employ Random search, Bayesian optimization or Grid search.
  • Feature Engineering: We carried out this process by removing unimportant features or by developing new.
  • Integration: To enhance the forecasting process, integrated frameworks are utilized by us.
  1. Deployment:

For actual time forecasting process, we decide the appropriate environment such as local server, cloud server or combined into farm management software to carry out framework’s implementation after the successive performance of our system.

  1. Feedback Loop:

To get reviews from agronomists and farmers continuously, an innovative approach is developed by us. Sequentially, we reconstruct the framework based on the data that are accessible.

Key Factors:

  • Domain Knowledge: We can improve the precision and performance of our framework by associate ourselves with crop field professionals and agronomists.
  • Quality of Data: The utilized data must be a valid or authentic data related to our research field.
  • External Attributes: Unanticipated attributes such as diseases, economic factors and pests outbreaks are considered by us.
  • Ethical Considerations: We should make sure about the forecasting that must not be misunderstood or misemployed when we publicly transfer it.

By utilizing machine learning approaches, we assist scholars how farmers can efficiently predict their productions and create required modifications in their farming procedures. Because of ML, the resources are effectively used and the farmers yield greatest production. Our skilled experts carefully frame out the problem statement by focusing our research objective. A 100% of output will be derived for your work by our world class certified engineers.

Crop Yield Prediction Using Machine Learning Ideas

Crop Yield Prediction Using Machine Learning Projects 

1.Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Keywords:

Deep learning, machine learning, crop yield prediction.

In our work the authors have applied different machine learning methods to evaluate the crop yield in Rajasthan of India on five recognized crops. We implemented the methods like Random Forest, SVM, Gradient Descent, Long Short-Term Memory and Lasso regression methods. Out of these our Random Forest method gives the best outcome.

  1. Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize

Keywords: 

Irish potato, maize, air temperature, rainfall, crops yield, random forest, prediction, support vector machine, polynomial regression

Our work engages data mining approach to predicting future crop harvest by utilizing weather and yields historical data. We implement ML methods to predict crop harvest based on weather data and link the information around production trends. Then the gathered data were analyzed through RF, Polynomial Regression and Support Vector Regressor. We have to train and test the models. Our RF is the best method for crop yield prediction.

  1. Using machine learning for crop yield prediction in the past or the future

Keywords:

Crop simulation model, wheat, sunflower, DSSAT, neural network

Our paper discovers the effect of selection of predictive method, amount of data and data separating approaches on predictive performance by utilizing synthetic datasets. The dataset of farm simulated yield can analyze with different methods like regularized linear models, RF and ANN. We have to perform this analysis with keras for NN and R package for other methods. Our RF method out performs ANN and regularised linear method.

  1. Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape

Keywords:

Crop modeling, NDVI, satellite, Landsat, sentinel-2, winter wheat

Our paper examines the coupling of crop modelling and ML to enhance the yield prediction of WW and OSR. Our aim is to identify whether the coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] gives best outcome and accurate prediction with other methods not using LUE. Four various RF methods were given as inputs namely NDVI, climate variables, NDVI+ climate variables and LUE generated biomass + climate variables to identify best predictor. Our combined NDVI +climate variable gives best outcome.

  1. A Comparative Analysis of the Machine Learning Model for Crop Yield Prediction in Quezon Province, Philippines

Keywords:

Decision Tree, Gaussian Process Regression, Ensemble

The goal of our paper is to regulate which ML method gives accurate crop yield prediction by the comparative analyse of following ML methods such as SVM, DT, Gaussian Process Regression, Ensemble and Neural Network. We have to train and test the dataset to analyze the best method to predict crop yield. After changing the hyperparameter of various methods GPR performs well than other models.

  1. Optimizing LSTM and Bi-LSTM models for crop yield prediction and comparison of their performance with traditional machine learning techniques

Keywords:

Long Short-Term Memory (LSTM), Support Vector Regression, Auto Regressive Integrated Moving Average (ARIMA), Vector Auto-regression (VAR)

Our paper offer a generic methodology to organize the fine tune the state-of-the-art LSTM based DL method over hyper parameterized optimization for prediction of yield based on multiple independent variables identified by utilizing multicollinearity test. We used Monte Carlo cross-validation method to verify the optimized LSTM method. The performance of Bi-LSTM method can compare with the performance of traditional ML methods like SVR and SVR polynomial, ARIMA and ARIMAX and VAR. But our LSTM performs better than other traditional ML methods.

  1. Review Study of Contemporary Work in Crop Yield Prediction Using Machine Learning Models

Keywords:

Machine learning in agriculture, Deep neural networks

Our paper evaluates the current work done in crop yield prediction by utilizing different ML methods has been offered. Our review debate machine learning methods, metrics, environmental data sets employed, research gaps and future directions. Time series-based deep neural network in combination with CNN were consider to be maximum in early decade. We also compare the performance analysis of DL versus traditional ML methods.

  1. A Next-Generation Device for Crop Yield Prediction Using IoT and Machine Learning

Keywords:

ANN, Fuzzy Logic, IoT

We present a next-generation device for crop yield prediction that uses IoT and ML methods. The device was executed and tested and that give high accuracy in predicting crop yields. We utilized a combination of three ML methods like ANN, Fuzzy Logic and SVM. The IoT sensors in the device can collect data on different environmental and soil conditions. We utilize the ANN method to analyse the sensor data and extract features. Fuzzy logic is utilized to uncertainty in data and SVM for classification.

  1. IoT and Machine Learning-Based Soil Quality and Crop Yield Prediction for Agricultural System

Keywords:

IoT sensors, Prediction, KNN, XGBoost, NPK, pH, Soil moisture

We concentrate on developing a portal that presents farmers to market their crops through the system and offer a direct farmer-buyer connection. The hardware based IoT system, various IoT sensors were utilized to detect soil quality integrated with ML methods like KNN and XGBoost that fed with real time data to predict crop yields. The XGBoost based crop prediction model gives the best accuracy.

  1. Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil (Lens culinarisMedik.)

Keywords: 

Soft computing; MARS; hybrid approach

Our study presents a novel hybrid method by integrating ML methods with feature selection for efficient modeling and complex phenomenon directed by multifactorial and non-linear behaviors. We tried to harness the advantage of soft computing methods MARS for feature selection that joined with SVR and ANN for effectively mapping the relation between predictor variable MARS-ANN and MARS-SVR hybrid structure. Our proposed MARS based hybrid method performs best.