Crop breeding is crucial in improving agricultural productivity while
potentially decreasing land usage, greenhouse gas emissions, and water
consumption. However, breeding programs are challenging due to long turnover
times, high-dimensional decision spaces, long-term objectives, and the need to
adapt to rapid climate change. This paper introduces the use of Reinforcement
Learning (RL) to optimize simulated crop breeding programs. RL agents are
trained to make optimal crop selection and cross-breeding decisions based on
genetic information. To benchmark RL-based breeding algorithms, we introduce a
suite of Gym environments. The study demonstrates the superiority of RL
techniques over standard practices in terms of genetic gain when simulated in
silico using real-world genomic maize data.

利用强化学习技术对模拟的作物育种计划进行优化，以此来改善农业生产力并减少土地使用、温室气体排放和水消耗。使用真实基因组玉米数据对基于强化学习的育种算法与标准育种方法进行对比，研究表明强化学习技术在遗传增益方面具有优势。

强化学习优化育种计划

Breeding Programs Optimization with Reinforcement Learning

The concept of sustainable intensification in agriculture necessitates the
implementation of management practices that prioritize sustainability without
compromising productivity. However, the effects of such practices are known to
depend on environmental conditions, and are therefore expected to change as a
result of a changing climate. We study the impact of crop diversification on
productivity in the context of climate change. We leverage heterogeneous Earth
Observation data and contribute a data-driven approach based on causal machine
learning for understanding how crop diversification impacts may change in the
future. We apply this method to the country of Cyprus throughout a 4-year
period. We find that, on average, crop diversification significantly benefited
the net primary productivity of crops, increasing it by 2.8%. The effect
generally synergized well with higher maximum temperatures and lower soil
moistures. In a warmer and more drought-prone climate, we conclude that crop
diversification exhibits promising adaptation potential and is thus a sensible
policy choice with regards to agricultural productivity for present and future.

在未来变暖和更干旱的气候下，农作物多样性可以提高作物的净初级生产力，从而有望成为现在和未来农业生产力的明智政策选择。

作物多样化对气候变化影响的理解：一种机器学习方法

Understanding the impacts of crop diversification in the context of  climate change: a machine learning approach

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI
AI) project demonstrates the importance of sound data work as a precondition
for the delivery of useful, localized datacentric solutions for public good
tasks such as agricultural productivity and food security. Drone collected data
and machine learning are utilized to determine crop stressors. Data, model and
the final app are developed jointly and made available to local farmers via a
desktop application.

本文介绍了利用人工智能技术和数据处理技术对加纳腰果病进行分析和诊断，达到提高农业生产和粮食安全的目的。