Ramos-Ospina, M.; Gomez, L.; Trujillo, C.; Marulanda-Tobón, A. Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves. Electronics2024, 13, 16.
Ramos-Ospina, M.; Gomez, L.; Trujillo, C.; Marulanda-Tobón, A. Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves. Electronics 2024, 13, 16.
Ramos-Ospina, M.; Gomez, L.; Trujillo, C.; Marulanda-Tobón, A. Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves. Electronics2024, 13, 16.
Ramos-Ospina, M.; Gomez, L.; Trujillo, C.; Marulanda-Tobón, A. Deep Transfer Learning for Image Classification of Phosphorus Nutrition States in Individual Maize Leaves. Electronics 2024, 13, 16.
Abstract
Computer vision is a powerful technology that has enabled solutions in various fields by analyzing visual attributes in images. One field that has taken advantage of computer vision is agricultural automation, which promotes high-quality crop production. The nutritional status of a crop is a crucial factor in determining its productivity. This status is mediated by approximately 14 chemical elements acquired by the plant, and their determination plays a pivotal role in farm management. To address the timely identification of nutritional disorders, this study focuses on the classification of three levels of phosphorus deficiencies through individual leaf analysis. The methodological steps include: (1) generating a database with laboratory-grown maize plants that were induced to total phosphorus deficiency, medium deficiency, and total nutrition, using different capture devices; (2) processing the images with state-of-the-art transfer learning architectures (i.e. VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2); and (3) evaluating the classification performance of the models using the created database. The results show that the VGG16 model achieves superior performance, with 98% classification accuracy. However, the other studied architectures also demonstrate competitive performance and are considered state-of-the-art automatic leaf deficiency detection tools. The proposed method can be a starting point to fine-tune machine vision-based solutions tailored for real-time monitoring of crop nutritional status.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
