Journal of Remote Sensing & GIS

Machine Learning Techniques for Crop Yield Prediction using Remote Sensing

Kun Cao^* Department of Geographical Science, Beijing Normal University, Beijing, China
^*Correspondence: Kun Cao, Department of Geographical Science, Beijing Normal University, Beijing, China, Email:

Received: 03-Jul-2023, Manuscript No. JGRS-23-22507; Editor assigned: 07-Jul-2023, Pre QC No. JGRS-23-22507 (PQ); Reviewed: 21-Jul-2023, QC No. JGRS-23-22507; Revised: 26-Jul-2023, Manuscript No. JGRS-23-22507 (R); Published: 04-Aug-2023, DOI: 10.35248/2469-4134.23.12.305

Description

Crop yield prediction plays a crucial role in agricultural decisionmaking, helping farmers optimize resource allocation and maximize productivity. The integration of remote sensing data with machine learning algorithms has revolutionized the accuracy and efficiency of crop yield prediction. This study aims to compare and evaluate various machine learning algorithms for crop yield prediction using remote sensing data, with the goal of identifying the most effective model.

Remote sensing in agriculture

Remote sensing technology enables the collection of valuable data about the Earth's surface from space, aircraft, or groundbased sensors. In agriculture, remote sensing data is typically gathered using satellites or Unmanned Aerial Vehicles (UAVs). These data sources provide crucial information about vegetation health, soil moisture and weather conditions among other variables which influence crop growth and yield.

Machine learning algorithms

The machine learning algorithms selected for this comparative study are as follows

Support Vector Machines (SVM): SVM is a supervised learning algorithm that can handle both linear and nonlinear data by transforming it into a higher-dimensional space. It aims to find an optimal hyper plane that best separates data points of different classes.

Random Forest (RF): RF is an ensemble learning method that constructs multiple decision trees during training and combines their outputs to make predictions. It is robust, handles highdimensional data well and can handle nonlinear relationships effectively.

Gradient Boosting Machines (GBM): GBM is another ensemble learning technique that builds multiple weak learners (usually decision trees) sequentially, with each new tree focusing on the errors made by the previous ones. GBM is known for its high accuracy and ability to handle complex interactions.

Artificial Neural Networks (ANN): ANN is a powerful deep learning model inspired by the human brain's structure. It consists of multiple interconnected layers of neurons that process and learn from data. ANN can handle complex patterns and nonlinear relationships in data.

Data collection and pre-processing

To conduct the comparative study, remote sensing data from multiple agricultural regions is collected over several growing seasons. The data includes spectral indices like Normalized Difference Vegetation Index (NDVI), weather variables, and historical crop yield records. The data is pre-processed to remove noise, fill missing values, and standardize the features to ensure fair comparison across algorithms.

Performance metrics

To assess the performance of each machine learning algorithm, several metrics are used

Mean Absolute Error (MAE): MAE measures the average absolute difference between predicted and actual crop yields, providing an understanding of the model's overall accuracy.

Root Mean Square Error (RMSE): RMSE estimates the square root of the average of squared differences between projected and actual crop yields. It penalizes large errors more heavily than MAE.

R-Squared (R²): R-squared measures the proportion of variance in the dependent variable (crop yield) that can be explained by the independent variables (remote sensing data). A higher R² indicates a better-fitted model.

The pre-processed data, the results are analyzed and compared. The performance metrics are used to evaluate the accuracy and robustness of each model in predicting crop yields. The study may reveal that certain algorithms perform better in specific agricultural regions or under particular weather conditions. SVM might excel in cases where data is linearly separable, while RF and GBM could outperform in scenarios with complex interactions and nonlinearity. ANN being a deep learning model, may show exceptional performance in areas with abundant data and complex patterns. This study provides valuable insights into the strengths and weaknesses of different machine learning algorithms for crop yield prediction using remote sensing data. Farmers and agricultural stakeholders can use this information to make informed decisions and adopt the most suitable model for their specific needs and conditions. As remote sensing technology and machine learning continue to advance crop yield prediction accuracy is expected to improve further ultimately contributing to sustainable agriculture and food security.