Computer vision and deep learning in insects for food and feed production: A review

TY - JOUR

T1 - Computer vision and deep learning in insects for food and feed production: A review

AU - Nawoya, Sarah

AU - Ssemakula, Frank

AU - Akol, Roseline

AU - Geissmann, Quentin

AU - Karstoft, Henrik

AU - Bjerge, Kim

AU - Mwikirize, Cosmas

AU - Katumba, Andrew

AU - Gebreyesus, Grum

PY - 2024/1

Y1 - 2024/1

N2 - Commercial insect production is a relatively new field that has gained traction in recent years due to its potential as a sustainable source of protein. Despite its promising future, the industry is still in its infancy, and there is much room for improvement in terms of production efficiency. To achieve this, it is essential to implement advanced technologies that can aid in process management. Recent progress in fields such as computer vision (CV) and machine learning has opened up numerous possibilities within insect rearing, encompassing automatic detection, identification, classification, as well as monitoring and tracking. These applications find relevance in automating insect production processes, ensuring insect product quality as well as environmental monitoring and control. The primary objective of this article is to highlight the potential of CV and deep learning (DL) in the domain of insect production for food and feed. It provides an in-depth overview of the key developments in this domain, shedding light on both challenges and opportunities. The article also presents various systems, accompanied by real-world examples and recent advancements, including the integration of machine learning. In conclusion, the article underscores the substantial potential of CV and machine learning to enhance the efficiency and productivity of insect production while identifying areas that warrant further research to advance the insect production sector.

AB - Commercial insect production is a relatively new field that has gained traction in recent years due to its potential as a sustainable source of protein. Despite its promising future, the industry is still in its infancy, and there is much room for improvement in terms of production efficiency. To achieve this, it is essential to implement advanced technologies that can aid in process management. Recent progress in fields such as computer vision (CV) and machine learning has opened up numerous possibilities within insect rearing, encompassing automatic detection, identification, classification, as well as monitoring and tracking. These applications find relevance in automating insect production processes, ensuring insect product quality as well as environmental monitoring and control. The primary objective of this article is to highlight the potential of CV and deep learning (DL) in the domain of insect production for food and feed. It provides an in-depth overview of the key developments in this domain, shedding light on both challenges and opportunities. The article also presents various systems, accompanied by real-world examples and recent advancements, including the integration of machine learning. In conclusion, the article underscores the substantial potential of CV and machine learning to enhance the efficiency and productivity of insect production while identifying areas that warrant further research to advance the insect production sector.

KW - Computer vision

KW - Deep learning

KW - Insects for food & feed

KW - Phenotyping

KW - Selective breeding

UR - http://www.scopus.com/inward/record.url?scp=85179621599&partnerID=8YFLogxK

U2 - 10.1016/j.compag.2023.108503

DO - 10.1016/j.compag.2023.108503

M3 - Review

SN - 0168-1699

VL - 216

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

M1 - 108503

ER -