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

Agriculture, Journal Year: 2024, Volume and Issue: 14(8), P. 1225 - 1225

Published: July 25, 2024

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

Language: Английский

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Potato: from functional genomics to genetic improvement

Molecular Horticulture, Journal Year: 2024, Volume and Issue: 4(1)

Published: Aug. 19, 2024

Potato is the most widely grown non-grain crop and ranks as third significant global food following rice wheat. Despite its long history of cultivation over vast areas, slow breeding progress environmental stress have led to a scarcity high-yielding potato varieties. Enhancing quality yield tubers remains ultimate objective breeding. However, conventional has faced challenges due tetrasomic inheritance, high genomic heterozygosity, inbreeding depression. Recent advancements in molecular biology functional studies provided valuable insights into regulatory network physiological processes facilitated trait improvement. In this review, we present summary identified factors genes governing growth development, along with genomics adoption new technologies for Additionally, explore opportunities improvement, offering future avenues research.

Language: Английский

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Using UAV Images and Phenotypic Traits to Predict Potato Morphology and Yield in Peru

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 1876 - 1876

Published: Oct. 24, 2024

Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of study is develop an innovative predictive model estimate yield and morphological quality, such as circularity length–width ratio potato tubers, based on phenotypic characteristics plants data captured through spectral cameras equipped UAVs. For purpose, experiment was carried out at Santa Ana Experimental Station in central Peruvian Andes, where clones were planted December 2023 under three levels fertilization. Random Forest, XGBoost, Support Vector Machine models used predict quality parameters, ratio. The results showed that Forest XGBoost achieved high accuracy prediction (R2 > 0.74). contrast, less accurate, with standing most reliable = 0.55 for circularity). Spectral significantly improved capacity compared agronomic alone. We conclude integrating indices multitemporal into estimating certain traits, offering key opportunities optimize agricultural management.

Language: Английский

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Cutting carbon and nitrogen footprints of maize production by optimizing nitrogen management under different irrigation methods

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: Dec. 4, 2024

Analyzing the effects of nitrogen (N) fertilizer application and water management on carbon (C) N footprints is vital to maize production systems.

Language: Английский

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