Deep Convolutional Neural Network for Plant Seedlings Classification

TL;DR

This paper demonstrates that deep convolutional neural networks can effectively classify plant seedlings, offering a promising tool for agricultural automation to enhance crop yield and efficiency.

Contribution

It introduces a CNN-based approach for plant seedling classification and compares its performance with traditional algorithms using a novel dataset.

Findings

CNN outperforms traditional algorithms in seedling classification accuracy

Deep learning models can significantly improve agricultural automation

The dataset includes 4,275 images of 12 plant species at various growth stages

Abstract

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.