Automatic Microplot Localization Using UAV Images and a Hierarchical Image-Based Optimization Method

Abstract

To develop new crop varieties and monitor plant growth, health, and traits, automated analysis of aerial crop images is an attractive alternative to time-consuming manual inspection. To perform per-microplot phenotypic analysis, localizing and detecting individual microplots in an orthomosaic image of a field are major steps. Our algorithm uses an automatic initialization of the known field layout over the orthomosaic images in roughly the right position. Since the orthomosaic images are stitched from a large number of smaller images, there can be distortion causing microplot rows not to be entirely straight and the automatic initialization to not correctly position every microplot. To overcome this, we have developed a three-level hierarchical optimization method. First, the initial bounding box position is optimized using an objective function that maximizes the level of vegetation inside the area. Then, columns of microplots are repositioned, constrained by their expected spacing. Finally, the position of microplots is adjusted individually using an objective function that simultaneously maximizes the area of the microplot overlapping vegetation, minimizes spacing variance between microplots, and maximizes each microplot’s alignment relative to other microplots in the same row and column. The orthomosaics used in this study were obtained from multiple dates of canola and wheat breeding trials. The algorithm was able to detect 99.7% of microplots for canola and 99% for wheat. The automatically segmented microplots were compared to ground truth segmentations, resulting in an average DSC of 91.2% and 89.6% across all microplots and orthomosaics in the canola and wheat datasets.

Segmentation of vegetation and microplots in aerial agriculture images: A survey

“…They are arranged in rows with fairly even spacing, but rows are offset from each other due to the sowing process. Mardanisamani et al (2021) proposed a three-step imagebased optimization approach to segment microplots. The authors used NDVI or ExG indices that obtained higher intensity value of vegetation than soil.…”

“…The results of the published study by (Mardanisamani et al., 2021) on the canola and wheat datasets. The results on the canola dataset (a) in early season, (b) mid‐season, (c) late‐season images.…”

“…Mardanisamani et al. (2021) proposed a three‐step image‐based optimization approach to segment microplots. The authors used NDVI or ExG indices that obtained higher intensity value of vegetation than soil.…”

“…Developing an automated method to accurately extract different microplot shapes with different sizes is needed and deep‐learning approaches could be used to provide a generalizable solution to address this need. Figure 4 shows the results of the published study by (Mardanisamani et al., 2021) on the canola and the wheat datasets in different growth stages.…”