Abstract：
The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quality, necessitating soil nutrient analysis for enhancing farm productivity and environmental sustainability. Researchers increasingly turn to Artificial Intelligence (AI) techniques to improve crop yield estimation and optimize soil nutrition management. This study reviews 155 papers published from 2014 to 2024, assessing the use of machine learning (ML) and deep learning (DL) in predicting soil nutrients. It highlights the potential of hyperspectral and multispectral sensors, which enable precise nutrient identification through spectral analysis across multiple bands. The study underscores the importance of feature selection techniques to improve model performance by eliminating redundant spectral bands with weak correlations to targeted nutrients. Additionally, the use of spectral indices, derived from mathematical ratios of spectral bands based on absorption spectra, is examined for its effectiveness in accurately predicting soil nutrient levels. By evaluating various performance measures and datasets related to soil nutrient prediction, this paper offers comprehensive insights into the applicability of AI techniques in optimizing soil nutrition management. The insights gained from this review can inform future research and policy decisions to achieve global development goals and promote environmental sustainability.
摘要：
联合国（UN）强调可持续农业在解决持续饥饿和通过全球发展到2030年实现零饥饿方面的关键作用。集约化的农业做法对土壤质量产生了不利影响，需要进行土壤养分分析以提高农场生产力和环境可持续性。研究人员越来越多地转向人工智能（AI）技术，以改善作物产量估算并优化土壤营养管理。这项研究回顾了2014年至2024年发表的155篇论文，评估了机器学习（ML）和深度学习（DL）在预测土壤养分中的应用。它突出了高光谱和多光谱传感器的潜力，通过多个波段的光谱分析实现精确的营养鉴定。该研究强调了特征选择技术的重要性，通过消除与目标营养素的弱相关性的冗余光谱波段来提高模型性能。此外,使用光谱指数，从基于吸收光谱的光谱带的数学比率得出，检查其在准确预测土壤养分水平方面的有效性。通过评估与土壤养分预测相关的各种绩效指标和数据集，本文对人工智能技术在优化土壤营养管理中的适用性提供了全面的见解。从这次审查中获得的见解可以为实现全球发展目标和促进环境可持续性的未来研究和政策决策提供信息。
