The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review

Rui-Feng Wang and Wen-Hao Su ()
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Rui-Feng Wang: College of Engineering, China Agricultural University, 17 Qinghua Donglu, Haidian, Beijing 100083, China
Wen-Hao Su: College of Engineering, China Agricultural University, 17 Qinghua Donglu, Haidian, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 8, 1-30

Abstract: The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain.

Keywords: potato; deep learning; pest and diseases; yield prediction; water and nutrient management (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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