This study examines how artificial intelligence (AI), especially
Reinforcement Learning (RL), can be used in farming to boost crop yields,
fine-tune nitrogen use and watering, and reduce nitrate runoff and greenhouse
gases, focusing on Nitrous Oxide (N$_2$O) emissions from soil. Facing climate
change and limited agricultural knowledge, we use Partially Observable Markov
Decision Processes (POMDPs) with a crop simulator to model AI agents'
interactions with farming environments. We apply deep Q-learning with Recurrent
Neural Network (RNN)-based Q networks for training agents on optimal actions.
Also, we develop Machine Learning (ML) models to predict N$_2$O emissions,
integrating these predictions into the simulator. Our research tackles
uncertainties in N$_2$O emission estimates with a probabilistic ML approach and
climate variability through a stochastic weather model, offering a range of
emission outcomes to improve forecast reliability and decision-making. By
incorporating climate change effects, we enhance agents' climate adaptability,
aiming for resilient agricultural practices. Results show these agents can
align crop productivity with environmental concerns by penalizing N$_2$O
emissions, adapting effectively to climate shifts like warmer temperatures and
less rain. This strategy improves farm management under climate change,
highlighting AI's role in sustainable agriculture.

本研究探讨了人工智能（AI），尤其是强化学习（RL）在农业中的应用，以提高作物产量、精确调整氮肥使用和浇水量、减少硝酸盐径流和温室气体排放为重点，特别关注来自土壤的氧化亚氮（N2O）排放。通过使用部分可观察马尔可夫决策过程（POMDP）和作物模拟器来模拟 AI 智能体与农业环境的互动，应用基于循环神经网络（RNN）的深度 Q 学习和 Q 网络进行智能体的训练，在模拟器中整合了机器学习（ML）模型来预测 N2O 排放。我们通过概率机器学习方法处理了 N2O 排放估算中的不确定性，并通过随机天气模型应对气候变异，提供一系列排放结果以提高预测可靠性和决策能力。通过纳入气候变化因素，提升了智能体的气候适应能力，旨在实现可持续农业实践。研究结果表明，通过惩罚 N2O 排放，在气候变暖和降水减少等气候变化条件下，这些智能体能够平衡作物生产和环境问题。这种策略改善了气候变化下的农业管理，突出了人工智能在可持续农业中的作用。

考虑不确定性的气候变异和 N$_2$O 排放的智能农业管理

Intelligent Agricultural Management Considering N$_2$O Emission and  Climate Variability with Uncertainties

The prediction of crop yields internationally is a crucial objective in
agricultural research. Thus, this study implements 6 regression models (Linear,
Tree, Gradient Descent, Gradient Boosting, K- Nearest Neighbors, and Random
Forest) to predict crop yields in 196 countries. Given 4 key training
parameters, pesticides (tonnes), rainfall (mm), temperature (Celsius), and
yield (hg/ha), it was found that our Random Forest Regression model achieved a
determination coefficient (r^2) of 0.94, with a margin of error (ME) of .03.
The models were trained and tested using the Food and Agricultural Organization
of the United Nations data, along with the World Bank Climate Change Data
Catalog. Furthermore, each parameter was analyzed to understand how varying
factors could impact overall yield. We used unconventional models, contrary to
generally used Deep Learning (DL) and Machine Learning (ML) models, combined
with recently collected data to implement a unique approach in our research.
Existing scholarship would benefit from understanding the most optimal model
for agricultural research, specifically using the United Nations data.

利用 6 种回归模型（线性回归、决策树、梯度下降、梯度提升、K 最近邻和随机森林）预测 196 个国家的农作物产量，并通过农药、降雨量、温度和产量 4 个参数，发现随机森林回归模型的决定系数（r^2）为 0.94，误差（ME）为 0.03。研究使用联合国粮食农业组织的数据和世界银行的气候变化数据训练和测试模型，并分析各个参数对农作物产量的影响。通过使用非传统的模型以及最近收集的数据，实现了独特的研究方法，旨在为农业研究找到最优模型，并利用联合国的数据进行研究。