# Deep Convolutional Neural Network for Plant Seedlings Classification

arXiv: https://arxiv.org/abs/1811.08404 · HTML: https://mlsanity.com/paper/1811.08404/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images](https://mlsanity.com/paper/2109.02716/) (2021)
- [Weed Recognition using Deep Learning Techniques on Class-imbalanced Imagery](https://mlsanity.com/paper/2112.07819/) (2021)

## Abstract (arXiv)

Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies. With increasing demand for food and cash crops, due to a growing global population and the challenges posed by climate change, there is a pressing need to increase farm outputs while incurring minimal costs. Previous machine vision technologies developed for selective weeding have faced the challenge of reliable and accurate weed detection. We present approaches for plant seedlings classification with a dataset that contains 4,275 images of approximately 960 unique plants belonging to 12 species at several growth stages. We compare the performances of two traditional algorithms and a Convolutional Neural Network (CNN), a deep learning technique widely applied to image recognition, for this task. Our findings show that CNN-driven seedling classification applications when used in farming automation has the potential to optimize crop yield and improve productivity and efficiency when designed appropriately.