Corn and Wheat Plant Identification on Radar and Optical Image Data

Year 2025, Volume: 4 Issue: 1, 7 - 17, 30.06.2025

Mustafa Öğretmen , Abdülkadir Gümüşçü

https://doi.org/10.5281/zenodo.15735400

Abstract

In recent years, prediction, detection, and classification applications have been made in many fields such as agriculture, health, stock market, economy, cybersecurity, etc., in Machine Learning and Artificial Intelligence. These applications are user-friendly and provide fast, high-quality, and accurate results. The advancements in these fields have shown that machine learning and deep learning methods are very useful in classifying large and complex data, especially when human brain and physical power are insufficient. Today's findings suggest there have been promising studies using these models, focused on time- and cost-effective and high-quality products. These studies provide efficiency in agricultural areas, thereby guiding both farmers and policymakers. In addition, the development and widespread implementation of unmanned aerial vehicles (UAVs) accelerated the process of obtaining multispectral aerial images. With the combined use of these technologies and high-speed computer software and hardware for precise and high-quality production in agriculture, it was possible to determine plant species and increase product quality. In this study, a dataset consisting of radar and optical image data was used to classify corn and wheat crops cultivated in agricultural areas. Four different machine learning models, namely Decision Tree (DT), K-Nearest Neighbors (K-NN), Naive Bayes (NB), and Support Vector Machines (SVM), were trained and compared on the dataset consisting of 174 features from Winnipeg, Canada. The dataset has been divided into 80% for training and 20% for testing. According to the results, the SVM model performed the best with the highest accuracy (0.9998) and F1-Score (0.9996), while the NB model performed the worst accuracy (0.9895) and F1-Score (0.9835). The detection of wheat and corn crop types by processing radar and optical image data with machine learning models has shown that other crops in cultivated lands in the Southeastern Anatolia Project (GAP) region can be classified using the same method, which shows the importance of this study.

Keywords

machine learning , unmanned aerial vehicle , multispectral image , agriculture , plant classification

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