High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

TL;DR

This paper introduces DeepStand, a deep learning model utilizing a truncated VGG-16 backbone for efficient, high-throughput counting of corn plants from images, improving accuracy over existing methods and aiding agricultural phenotyping.

Contribution

The paper presents a novel deep learning approach, DeepStand, that effectively counts corn stands in images, addressing scale variation and outperforming current state-of-the-art techniques.

Findings

DeepStand accurately counts corn stands in various conditions.

The method outperforms existing state-of-the-art approaches.

It enables high-throughput phenotyping with reduced labor.

Abstract

The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. However, due to the shear size of a breeding program and current resource limitations, the ability to collect precise data on individual plants is not possible. In particular, efficient phenotyping of crops to record its color, shape, chemical properties, disease susceptibility, etc. is severely limited due to labor requirements and, oftentimes, expert domain knowledge. In this paper, we propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. The proposed method adopts a truncated VGG-16 network as a backbone feature extractor and merges multiple feature maps with different scales to make the network robust against scale variation. Our extensive computational experiments suggest that our proposed method can successfully count corn stands and out-perform other state-of-the-art methods. It is the goal of our work to be used by the larger agricultural community as a way to enable high-throughput phenotyping without the use of extensive time and labor requirements.