Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

Abstract

Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

“…As those technologies become more ubiquitous, more fruit descriptors and novel assessment systems can be developed based on the 3D architecture of strawberries. Although SfM methods were applied to high-spatial-resolution RGB images [129,130] to calculate several strawberry canopy parameters (e.g., canopy area, average height, volume, and smoothness), LiDAR could be used to obtain detailed information about a strawberry plant's structural properties. For example, Jiang et al [166] analyzed LiDAR data and proposed various quantification factors for the bush architecture of blueberries, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices).…”

“…Three of the variables were used to predict the leaf area (R 2 = 0.79) and dry biomass (R 2 = 0.84) throughout the strawberry-growing season using multiple linear regression analysis. Abd-Elrahman et al [130] built on this study by developing automated canopy delineation and canopy size metric extraction models to predict strawberry biomass at greater throughput. Takahashi et al [131] applied Kinect (the depth sensor used in the Microsoft XBOX console) to detect plant height and leaf area receiving direct sunlight at different leaf layers over time under different environments.…”

Improving Strawberry Yield Prediction by Integrating Ground-Based Canopy Images in Modeling Approaches

“…Canopy information extraction was performed automatically using geospatial analysis models. Although these models can analyze thousands of plants in a few hours [31], we believe that the models have the potential to be served as a server service for web clients as a step towards commercial implementation. Flower and fruit counting was conducted manually to allow visual identification of the flowers and fruits on images captured by the platform from different directions (multi-view) and projecting them to the orthorectified mosaic.…”

“…In this section, the methods and assumptions used to extract the geometrical properties of the canopies are introduced. These methods have also been assessed and discussed in Abd-Elrahman et al [31]. The ESRI's ArcMap software v10.3 [21] was used to analyze the RGB-IR orthomosaics and DSM resulting from the SfM analysis.…”

High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation

“…Some studies have computed new phenotypes using automated methods. These include traits such as achene number (Le Louëdec & Cielniak, 2021b), fruit volume (He et al., 2017) and canopy height (Abd‐Elrahman et al., 2020), which are highly quantitative and have the potential to be useful for breeding applications, but would traditionally have been too costly and time‐consuming to measure by hand. Features of the canopy, such as how the leaves are presented and the relationship between flowers and canopy, also have the potential to be important for novel breeding targets, such as suitability for robotic harvest.…”