Crop Yield Prediction Using Machine Learning

Professionals in make use of correct algorithm and we imply modern methodologies and craft a perfect Crop Yield Prediction Using Machine Learning project for you. Share with us all your technical details and your research issues we will guide you until publication of your paper. By means of machine learning, crop yield prediction includes the application of algorithms that observe historical data and detect patterns which is possible for forecasting future crop yields. This operation involves broad range of data types, incorporating:

  • Weather data like temperature, precipitation and humidity etc.
  • Soil properties such as type, pH level, nutrients, moisture and many more.
  • Crop kinds and categories
  • Past yield data
  • Satellite and drone images
  • Farming practices like irrigation, fertilization and crop rotation etc.

The following machine learning models are naturally accomplished for crop yield prediction:

  1. Data Collection: We fetch the past data on yields and the several factors that perhaps affect the yields.
  2. Data Preprocessing: Data’s are purifying and arranging the data into appropriate structure for observations. This includes managing the missed data, standardizing data or developing novel attributes that possibly result in more prediction of yields.
  3. Feature Selection: The features (variables) are detected that are very essential for forecasting the crop yields. It incorporates mathematical methods to understand the relationship between features and yields or we employ the feature selection algorithms.
  4. Model Selection: Machine learning models must be select accurately by us. Generally decisions involve regression models like linear and polynomial, decision trees, random forests, support vector machines, neural networks and ensemble techniques.
  5. Training: We boost the pre-processed data into the model for the learning process from the historical instances.
  6. Validation: Utilizing an individual set of data that the model does not visible earlier for examining the precision of our model.
  7. Testing: Following the validation process, we re-evaluate the model with a varied set of data to check that it is finely established to fresh data.
  8. Deployment: After finishing the training and testing process of our model, it is ready to be implemented in real-world applications for predicting regarding the future crop yields.
  9. Monitoring and Updating: The model performance is constantly observed by us that contain capacity to upgrade the model with fresh data.

Over here, we provide some of the machine learning techniques for crop yield prediction:

  • Linear Regression: We establish this easy method that proceeds when the correlation between feature and the yield is even.
  • Polynomial Regression: Incase, the relationship between the features and yield is not sequential, this method is beneficial for us.
  • Random Forest: This is an ensemble learning technique which is more efficient for us in solving regression problems.
  • Gradient Boosting Machines (GBM): It is the another form of ensemble methods that construct trees in an serial style , where each tree attempts to proper the anomalies of the earlier one .
  • Neural Networks: We utilize this deep learning model that catches the critical non-sequential correlations between features and yields.
  • Convolutional Neural Networks (CNNs): It is specifically valuable for us, in that input data involves images which are extracted from satellites or drones.
  • Recurrent Neural Networks (RNNs): When there is a temporary element to the data like time-series weather data, we deploy this type of network.

Always remember that crop yield prediction is a difficult task and selection of models along with perfection of the prediction is highly based on the property and roughness of the obtainable data. Moreover, some exterior factors like sudden weather conditions, diseases and pests that importantly influence the yields are not entirely reported in the model.

Crop Yield Prediction Using Machine Learning Projects

Crop Yield Prediction Using Machine Learning Thesis Topics

Have a look at the recent of our work we provide you with latest ideas and innovative solutions.

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


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 recognised 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


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

Our work engage 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


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

Our paper discover 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


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


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


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 optimisation for prediction of yield based on multiple independent variables identified by utilizing multicollinearity test. We used Monte Carlo cross-validation method to verify the optimised 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


Machine learning in agriculture, Deep neural networks

Our paper evaluate 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


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


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.)


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 behaviours. 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.