# Rice Quality: A Multidimensional Evaluation Integrating Ecology, Management and Genetic Regulation

## TL;DR

This paper discusses how rice quality is evaluated by considering ecological, management, and genetic factors to meet modern demands for nutrition and safety.

## Contribution

The paper introduces a multidimensional framework integrating ecology, management, and genetics to enhance rice quality and safety.

## Key findings

- Rice quality assessment now includes nutritional value and safety in addition to appearance and processing quality.
- Combining genetic improvement and precise field management can enhance rice quality traits.
- Artificial intelligence can improve breeding cycles and pest management in rice production.

## Abstract

With global economic development and rising living standards, expectations regarding the quality of staple rice have become increasingly multifaceted. This shift has imposed more stringent demands on high-quality rice breeding and field management and has stimulated research into the mechanisms underlying changes in rice quality. This article explores how assessments of rice quality have evolved from a primary emphasis on appearance, eating and processing quality to include stronger requirements for nutritional value and safety. In rice production systems, quality outcomes are influenced by interactions among genetic traits, ecological factors and field management practices. Through genetic improvement, biological breeding techniques and precise field management, improvements in appearance, eating and nutritional qualities can be achieved. Although climate change is an uncontrollable external factor affecting rice quality, constructing multi-factor dynamic simulation models that target key genes has been proposed as a strategy to enhance stress resistance and guide rice breeding. Rice safety and quality depend on the rational use of pesticides in terms of timing and dosage, which can help mitigate disease and insect resistance while reducing the risks associated with pesticide residues and toxins. Furthermore, the application of artificial intelligence technologies in biological breeding and field management can shorten breeding cycles, improve disease and pest outbreak prediction and support the timely formulation of treatment prescriptions.