Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images
Chittathuru Himala Praharsha, Alwin Poulose, Chetan Badgujar · 2024
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Description
Chittathuru Himala Praharsha, Alwin Poulose, and Chetan Badgujar tested many learning methods for spotting tomato pests from photos in a 2024 study. Pest damage cuts tomato yields and costs farmers money, so naming the pest correctly is the first step of sound control. The team worked with 4,263 images covering eight common tomato pests.
They trained a custom neural network and compared six training optimizers, the settings that steer learning. One called RMSprop won, reaching about eighty-nine percent accuracy on held-out images. The team also ran six classic machine learning methods, from logistic regression to random forest, on the same images to see how the network stacked up against simpler tools.
Catalog record
| CREATOR | Chittathuru Himala Praharsha, Alwin Poulose, Chetan Badgujar |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 91 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |