New classification of weed development stages using machine learning methods: radiomics parameters

• Published in: Advances in Weed Science Vol. 43; p. e020250014
• Main Authors: Gülbandilar, Eyyüp, Uludağ, Ahmet, Çiçek, Yasin
• Format: Journal Article
• Language: English
• Published: Londrina Sociedade Brasileira da Ciência das Plantas Daninhas, UFV - Depto de Fitotecnia 01.01.2025; Sociedade Brasileira da Ciência das Plantas Daninhas - SBCPD
• Subjects: Accuracy; AGRONOMY; Artificial intelligence; Classification; Datasets; Decision trees; Deep learning; Developmental stages; Digitization; Herbicides; Image processing; Machine learning; Parameters; Radiomics; Regression models; Seeds; Software packages; Support vector machines; Weed control; Weeds; Workload; Workloads; Weed Development Stage; Weed; ROI; Classification; Radiomics
• ISSN: 2675-9462; 2675-9462
• DOI: 10.51694/AdvWeedSci/2025;43:00018

Background: In recent years, artificial intelligence methods based on image processing have been introduced for weed identification therefore weed control. Image processing methods typically cause a heavy computer workload.Objective: The goal of this research was to create a classification model for weed development stages with better accuracy reduced workload by incorporating Region of Interest (ROI) into existing classification models.Methods: Weeds were grown and photographed in several development stages for the dataset. Using the ROI technique, commonly utilized in medicine, the leaf image features were digitized, and a total of 448 sample records were obtained. Of these image features, 9 were identified as the most important variables using the linear regression model. SMOTE analysis was applied to balance the distribution in our data. The data were randomly divided into 70% for training, 15% for testing, and 15% for validation groups. The models were developed by using artificial intelligence methods such as ANFIS, MLPNN, SVM, kNN, Naive Bayes, Decision Tree, Random Forest, Deep Learning, and Logistic Regression.Results: Accuracy, Precision, Recall, and F1-score parameters were used to evaluate the performance of the models. NB, ANFIS, and LR models failed to produce results within acceptable limits. However, RF, MLPNN, DT, Keras, and SVM models were successful. kNN models results were not far off but a failure nonetheless.Conclusions: Based on these results, we demonstrated that our RF, MLPNN, DT, Keras, SVM, and kNN models with ROI implementation can successfully determine the development stages of weeds.