Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device
Studies reported that 12–40% of stored grains are lost due to insects, but the use of early detection devices such as acoustic sensors can guide subsequent storage management to reducing losses. Acoustic detection can directly identify the cause of damage (i.e., insects) in stored grains rather than...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/155309 |
Ejemplares similares: Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device
- Determining the sound signatures of insect pests in stored rice grain using an inexpensive acoustic system
- Insect attractants for enhanced monitoring and control of pests in rice storage
- Convolutional neural networks to assess bergamot essential oil content in the field from smartphone images
- Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images
- Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images
- Enhancing the quality of stored rice seeds and grains using hermetic storage grain bag in Assam, India