[Paper Review] Image based Crop Monitoring Technologies in Protected Horticulture: A Review

Namal Jayasuriya, Yi Guo+2 more|arXiv (Cornell University)|Jan 25, 2024

Smart Agriculture and AI5 citations

TL;DR

This paper reviews image-based crop monitoring and phenotyping platforms used in protected horticulture, detailing camera technologies, platform types, and trait extraction, and highlights gaps for tall, vertically-supported crops in semi-controlled environments.

ABSTRACT

Future food security is a major concern of the 21st century with the growing global population and climate changes. In addressing these challenges, protected cropping ensures food production year-round and increases crop production per land area by controlling environment conditions. Maintaining the growth and health of crops in these facilities is essential to ensure optimum food production. However, this is a laborious work and is currently done manually. Image-based non-destructive plant phenotyping is an emerging research area that reduces the skilled labour cost while enhancing the monitoring of crop growth, health, and identifying phenotype-genotype relations for plant breeding. With the proliferations of protected infrastructures and targeted plants, different technologies and sensor setups are needed for image-based crop monitoring. Conveyor-type plant-to-sensor systems, bench-top or gantry-based systems are commonly found in research facilities focussing on phenotyping of small, relatively short, or movable model plants. This review examines the literature on crop monitoring and phenotyping platforms in both field and protected facilities and explains different camera technologies and their ability to extract different plant traits. The review highlights the future research directions of image-based monitoring of commercial scale protected crops where crops can be relatively tall or vertically supported under semi controlled environments, which presents new challenges and is rarely covered in the literature.

Motivation & Objective

• Motivate and address food security through year-round protected cropping and improved crop health monitoring.
• Summarize image-based non-destructive phenotyping approaches used in protected facilities and fields.
• Identify sensor configurations and platform types (conveyor, bench-top, gantry) for plant trait extraction.
• Discuss challenges and gaps for tall, vertically-supported crops in semi-controlled environments.
• Propose directions for future research toward commercial-scale implementation.

Proposed method

• Review literature on crop monitoring and phenotyping platforms in field and protected facilities.
• Explain camera technologies and their capabilities for extracting plant traits.
• Describe common platform configurations (conveyor-type, bench-top, gantry-based) for small or movable plants.
• Contrast semi-controlled protected environments with field conditions in terms of image-based phenotyping.
• Highlight future research directions for handling taller crops in protected facilities.

Experimental results

Research questions

• RQ1 What image-based monitoring technologies are used in protected horticulture for crop phenotyping?
• RQ2 What camera configurations and platforms enable extraction of key plant traits in protected environments?
• RQ3 How do protected facilities differ from field settings in the context of image-based crop monitoring?
• RQ4 What are the gaps and future research directions for scaling image-based monitoring to commercial-height crops?

Key findings

• Different camera technologies are evaluated for extracting diverse plant traits in protected settings.
• Platform types such as conveyor, bench-top, and gantry systems are commonly used for small or movable plants in phenotyping studies.
• There is a gap in literature regarding image-based monitoring of tall or vertically-supported crops in semi-controlled environments.
• The review highlights the need for future research focused on commercial-scale protected crops beyond small-model plants.
• The paper discusses challenges and opportunities for adapting image-based phenotyping to taller crops and semi-controlled environments.