Current state and future perspectives of commercial strawberry production: A review

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning Simultaneous use of multiple sensors (eg, high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming

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

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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.

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

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an Adj R2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving Adj R2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p < 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning.

/pdf/radiative-transfer-image-simulation-using-l-system-modeled-1zwm5gso.pdf

Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies

Quantifying canopy biophysical parameters is critical to agricultural research and farm management. In this study, strawberry dry biomass and leaf area were modeled statistically using high spatial and temporal resolution imagery. A mobile field data acquisition system was used to acquire thousands of very high resolution (~0.5 mm) close-range images seven times throughout the strawberry growing season. Ortho-mosaics and dense point clouds were generated through Structure from Motion (SfM) and used in Object-Based Image Analysis (OBIA) at the sub-leaf level to extract canopy structure variables such as planimetric canopy area, canopy average height, and canopy smoothness metric. Regression analysis was carried out using these image-derived canopy variables as predictors to model leaf area ( R 2  = 0.79; ten-fold cross-validation RMSE = 0.056 m2) and dry biomass ( R 2  = 0.84; ten-fold cross-validation RMSE = 7.72 g) obtained through destructive measurements. Results indicate consistent predictive power through the season and across 17 strawberry genotypes. The study showed that the canopy smoothness metric developed in this study as an indicator of canopy density could complement other variables (planimetric canopy area, canopy average height) that describe canopy geometric properties.

Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images

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

Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies

Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images