In this Grants4Ag project, my group will use a deep learning model, a sub-area of machine learning, to predict and select disease-resistant plants by combining major genes and minor …

By combining aerial imaging, remote sensing, computer vision and deep learning techniques, we will develop dynamic models to monitor and predict key agronomic traits during the growth and …

Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

Dec 1, 2022 · Starting from data capturing methods and the availability of public datasets, the present paper provides a comprehensive review of vision-based machine learning techniques …

Jan 9, 2025 · This study used a combination of H2D-COS and transfer learning based on the CNN-LSTM algorithm to predict SOD activity in tomato leaves under various salt stress …

First, is the standard approach, where fine-grained recognition is posed as closed-set classification; the learning involves minimization of cross-entropy loss. Second, a retrieval …

Feb 1, 2024 · During the experiment, the halo thermal time model was utilized to investigate the germination of lentil seeds. The study consisted of five constant cardinal temperatures: 15, 20, …

May 6, 2024 · The outcomes from the recent halo thermal time model experiment underscore the significant impact of temperature and NaCl on the agronomic characteristics of Lens culinaris.

The paper introduces an innovative approach to plant disease detection through deep learning, specifically using the VGG19 model, which can scrutinize intricate patterns in plant images, …

The germination of lentil seeds was investigated during the experiment using the halo thermal time model. The study consisted of five constant cardinal temperatures: 15, 20, 25, 30, and …