Classification of plants in corn fields using machine learning techniques

Abstract

This thesis addresses the tasks of detecting vegetation and classifying plants into target crops and weeds using combinations of machine learning and pattern recognition algorithms and models. Solutions to these problems have many useful applications in precision agriculture, such as estimating the yield of a target crop or identifying weeds to help automate the selective application of weedicides and thereby reducing cost and pollution. The novel contribution of this work includes development and application of image processing and computer vision techniques to create training data with minimal human intervention, thus saving substantial human time and effort. All of the data used in this work was collected from corn fields and is in the RGB format. As part of this thesis, I first discuss several steps that are part of a general methodology and data science pipeline for these tasks, such as: vegetation detection, feature engineering, crop row detection, training data generation, training, and testing. Next, I develop software components for segmentation and classification subtasks based on extant image processing and machine learning algorithms. I then present a comparison of different classifier models developed through this process using their Receiver Operating Characteristic (ROC) curves. The difference in models lies in the way they are trained - locally or globally. I also investigate the effect of the altitude at which data is collected on the performance of classifiers. Scikit-learn, a Python library for machine learning, is used to train decision trees and other classification learning models. Finally, I compare the precision, recall, and accuracy attained by segmenting (recognizing the boundary of) plants using the excess green index (ExG) with that of a learned Gaussian mixture model. I performed all image processing tasks using OpenCV, an open source computer vision library.