Hybrid Multi-Scale Neural Network with Attention-Based Fusion for Fruit Crop Disease Identification
Shakhmaran Seilov, Akniyet Nurzhaubayev, Marat Baideldinov, Bibinur Zhursinbek, Medet Ashimgaliyev, Ainur Zhumadillayeva · 2025
Read Hybrid Multi-Scale Neural Network with Attention-Based Fusion for Fruit Crop Disease Identification by Shakhmaran Seilov, Akniyet Nurzhaubayev, Marat Baideldinov, Bibinur Zhursinbek, Medet Ashimgaliyev, Ainur Zhumadillayeva (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12734175.txt.
Description
Shakhmaran Seilov and co-authors build a sharper plant disease spotter in a 2025 paper. Fruit crop illnesses cost farmers dearly when they slip by, and walking the fields to check by eye is slow and unreliable at scale.
Their network looks at a leaf photo at several scales at once. A transformer branch reads the wide scene patterns while convolutional branches catch fine local marks, and an attention step fuses the two views by importance. The mix aims to handle diseases of many sizes against messy field backgrounds. It also tries to stay light enough for small devices on the farm. Better early spotting could cut losses across orchards and groves.
Catalog record
| CREATOR | Shakhmaran Seilov, Akniyet Nurzhaubayev, Marat Baideldinov, Bibinur Zhursinbek, Medet Ashimgaliyev, Ainur Zhumadillayeva |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |