Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Zhe Lin,Wenxuan Guo

doi:10.3390/rs13142822

Abstract

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

Highlights

An accurate plant stand count is a prerequisite to evaluating emergence rate, assessing seedling vigor and facilitating site-specific management
63% for the MobileNet model with 400 training images. These results are similar to a study that reported an mean average precision (mAP) of 86% using YOLOv3 with 200 labeled training images in predicting cotton stand count using unmanned aerial systems (UAS) images [25]
The values of mAP, average recall (AR) and mean F1-score increased by 8% and 19%, 25% and 33%, 12% and 18%, respectively, for the CenterNet and MobileNet models with 900 training images

Summary

Introduction

An accurate plant stand count is a prerequisite to evaluating emergence rate, assessing seedling vigor and facilitating site-specific management. Stand count is required to measure crop density and uniformity of seedlings for breeding programs [1,2,3]. Stand count is critical for growers to make decisions for replanting and other site-specific management to avoid yield loss [4,5]. Cotton (Gossypium hirsutum L.) yield rapidly decreases if plant density is below five plants per linear meter of a row in the Texas High. The traditional method for determining plant stand count is typically by manually counting the number of plants within a unit area, which is time consuming and labor intensive with sampling bias. Efficient and accurate stand counting methods are needed to expedite breeding pipelines or improve decision support in precision crop management.

Objectives

Methods

Results

Discussion

Conclusion