Food and Energy Security, Journal Year: 2025, Volume and Issue: 14(1)
Published: Jan. 1, 2025
ABSTRACT Plant phenomics deals with the measurement of plant phenotypes associated genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches quickly evaluate characteristics enhance yields crops smart facilities. HTP uses parameters for accuracy, such as software sensors, well hyperspectral imaging pigment data, thermal water content, fluorescence photosynthesis rates. They provide information on growth kinetics, physiological biochemical characteristics, genotype–environment interaction. Artificial intelligence (AI) machine learning (ML) are used large volume phenotypic data predict rates, determine optimal time plants, or detect diseases, nutrient deficiencies, pests at an early stage. The lighting factories is adjusted based specific phase using different light intensities, spectrums, durations germination, vegetative growth, flowering stages, hydroponics method providing nutrients, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) improving certain resistance drought. These systems crop production, yields, adaptability, input use by optimizing utilizing precision breeding techniques. AI combination several disciplines, promoting understanding plant–environment interactions relation problems resource use, climate change. It affects their capacity develop that capture inputs, minimize chemical application, resilient Phenomics cost‐effective, reduces contributes more sustainable agricultural practices, being economically environmentally sound. Altogether, central CEA due its capitalize potential within advance sustainability food security. Through phenomic research, next advancements likely be even revolutionary terms practices worldwide.
Language: Английский