Advancing Tassel Detection and Counting: Annotation and Algorithms

Azam Karami,Melba Crawford,Karoll Quijano

doi:10.3390/rs13152881

Abstract

Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.

Highlights

Maize is a major crop for food consumption and a source of material for a wide range of products
The unmanned aerial vehicles (UAVs) was flown at 20 m altitude, and the RGB imagery was processed to a 0.25 cm pixel resolution orthophoto using the method of [37] to eliminate inaccuracies due to lens distortion and double mapping associated with significant height changes over short distances
The tassel detection algorithms were implemented on a machine with seven cores, one GPU (GTX 1080ti, 11 GB RAM), and 128 GB external RAM

Summary

Introduction

Maize is a major crop for food consumption and a source of material for a wide range of products. Increasing maize yield is important, especially under the pressure of global climate change, which is often associated with increased temperatures and extreme droughts [1,2]. Plant breeders focused on developing improved varieties of crops seek to understand the joint impact of genetics, environment, and management practices on yield. For maize and many other grains crops, flowering is one of the most important stages as it initiates the stage of reproduction. Physical or biological, can cause plant damage and result in production losses. Traditional approaches for field-based monitoring of tasseling are manual and time-consuming, labor-intensive, expensive, and potentially error-prone, especially in large fields. Varying illumination and shape, shadows, occlusions, and complex backgrounds impact the accuracy of these approaches [3,4]

Methods

Results

Conclusion