Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds
Adel Bakhshipour, Shahriar Ramezanpour · 2025
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Description
Adel Bakhshipour and Shahriar Ramezanpour use special imaging to tell peanut plants apart from weeds in a 2025 study. Precise weed control needs fast, accurate spotting of weeds among crops. Their imaging captures many narrow bands of light, far more than a normal camera, and machine learning sorts the patterns.
They tested ways to clean the image data and to pick the most useful light bands from hundreds available. The best mix used a median filter, a selection method, and a classic classifier. It reached very high accuracy in training and strong accuracy on fresh test data, using only twelve chosen bands. The method separated peanuts from each of four weed species. The authors advise testing it under varied field conditions next.
Catalog record
| CREATOR | Adel Bakhshipour, Shahriar Ramezanpour |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |