Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Abstract

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13-15 (it is a scale to identify the phenological development stage of a plant which is here the 3-to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R 2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes.

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

“…Recent years have seen unmanned aerial systems (UAS) emerge as effective means for field-relevant phenotyping activities by enabling efficient and more affordable collection of aerial crop images over entire crop growth cycles. Traditional image analysis and machine learning have played key roles in transforming these image data into targeted phenotypic information such as plant height [1,2] and plant population counts [3][4][5]. However, the massive image data being collected by UAS and other high throughput platforms pose challenges to traditional methods, whose performance tends to level off and fall short of the high accuracy required for fully automated systems [6].…”

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

“…State of the art imaging hardware and image analysis methods have attracted considerable interest from the plant phenotyping community. This is not only due to their potential of relieving the burden of manual phenotyping, but also the possibility of objectively quantifying trait characteristics [3]. Land-based phenotyping platforms, such as the mobile ground platform (MGP) used in this study, are able to capture high resolution images of plant canopies at close range.…”

Assessing Lodging Severity over an Experimental Maize (Zea mays L.) Field Using UAS Images

“…On the other hand, accurate stand count based on early season imagery is one of the primary measurements that studies have been working to automate due to its importance as a metric for both farmers and researchers. Furthermore, it is believed to be a relatively straightforward metric to estimate using image processing algorithms [40,41]. Once these algorithms are readily used and implemented with a UAS surveying approach, estimation errors introduced by using seed count are expected to be eliminated.…”