A novel CNN gap layer for growth prediction of palm tree plantlings.

ABSTRACT: Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting p

SUBMITTER: Kumar TA 

PROVIDER: S-EPMC10420369 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature