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

Abstract

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 (e.g., 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.

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

“…Normally sensors used in RS that are for crop monitoring detect the following electromagnetic wave bands, depending on specific objectives [7]: The amplitude of the information retrieved from RS is considerable to support sustainable agriculture capable of feeding a rapidly growing world population. Among the prominent advantages or applications of RS are the identification of phenotypically better varieties, optimization of crop management, evapotranspiration, agriculture phenology, crop production forecasting, ecosystem services (related to soil or water resources) provision, plant and animal biodiversity screening, crop and land monitoring, and precision farming [8,18,19,[40][41][42].…”

“…[84] Visible RGB (VIS) Vegetation classification and estimation of geometric attributes. [42] Multispectral and hyperspectral Physiological and biochemical attributes (leaf area index, crop water content, leaf/canopy chlorophyll content, and nitrogen content). [85] Fluorescence spectroscopy and imaging sensors Chlorophyll and nitrogen content, nitrogen-to-carbon ratio, and leaf area index.…”

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

“…Computer vision methods have been successfully implemented to identify, count and capture the morphological features of plants [65]. An approach to semi-automated identification of plants was explored with a YOLO (You Only Look Once) neural network object detector.…”

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

“…These methods are often neither practical nor sustainable when targeting large-scale farm operations or research applications. Remote sensing technology allows for large amounts of data to be collected quickly, and the information facilitated by this technology provides a huge potential for extracting many variables quickly and non-destructively [12,13]. The remote sensing imagery is important to build strawberry yield prediction models that can be practically implemented for farm operations.…”

“…Biophysical parameters, such as leaf area and dry weight biomass of strawberry canopies, have been modeled using ground-based remote sensing imagery [13,14]. Lidar is often used for canopy modeling, but this technology is still very costly for extensive data acquisition throughout the growing season.…”