I-VIEW: AN IMAGE PROCESSING AND MACHINE LEARNING APPROACH FOR ACCURATE IDENTIFICATION OF SOYBEAN (Glycine max L.) CROP AND WEEDS
| dc.contributor.author | Ronny Angel B. Pastrana | |
| dc.date.accessioned | 2026-04-20T02:40:40Z | |
| dc.date.available | 2026-04-20T02:40:40Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In recent years, the classification of soybean plants and weeds has gained traction due to the increasing need for automated agricultural solutions. To address this, the study introduces I-VIEW, also know as Intelligent View, a deep learning-based approach using Convolutional Neutral Networks (CNNs) to classify soybean plants varieties, including Black, Brown Canada, Clsoy 1, Clsoy 2, Collection 1, Collection 2 and Tiwala 10 Soybean, incorporating advanced image processing techniques to improve accuracy and RGB image processing to prepare the dataset for training. The deep learning models for I-VIEW were developed using Python, a versatile and widely used programming language known for its robust ecosystem of machine learning libraries such as TensorFlow and Keras. the evaluation was conducted at three levels: overall classification accuracy on testing dataset, validation dataset, and real-world testing. The testing dataset achieved a perfect accuracy off 100% with no errors among 9000 images; the validation dataset attained accuracy rate of 94.7% with a 5.3% error rate. In real-world scenarios, the accuracy rate is 91% and 9% error rate. The proposed method outperforms previous relevant works and provides a reliable approach to identifying soybean plants and weeds. | |
| dc.identifier.uri | http://granarium.clsu.edu.ph/handle/123456789/1024 | |
| dc.language.iso | en_US | |
| dc.relation.supervisor | ROLDAN T. QUITOS, M.Sc. | |
| dc.title | I-VIEW: AN IMAGE PROCESSING AND MACHINE LEARNING APPROACH FOR ACCURATE IDENTIFICATION OF SOYBEAN (Glycine max L.) CROP AND WEEDS | |
| dc.type | Thesis |