The real-time detection of drought stress has major implications for preventing cash crop yield loss due to variable weather conditions and ongoing climate change. The most widely used indicator of drought sensitivity/tolerance in corn and soybean is the presence or absence of leaf wilting during periods of water stress. We develop a low-cost automated drought detection system using computer vision coupled with machine learning (ML) algorithms that document the drought response in corn and soybeans field crops. Using ML, we predict the drought status of crop plants with more than 80% accuracy relative to expert-derived visual drought ratings.

中文翻译：

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使用低成本计算机视觉系统和机器学习技术检测干旱压力

干旱压力的实时检测对于防止因天气条件变化和持续气候变化而导致的经济作物产量损失具有重要意义。玉米和大豆干旱敏感性/耐受性的最广泛使用的指标是在水分胁迫期间是否存在叶片萎蔫。我们使用计算机视觉和机器学习 (ML) 算法开发了一种低成本的自动化干旱检测系统，该系统记录了玉米和大豆大田作物的干旱反应。使用机器学习，相对于专家得出的视觉干旱评级，我们以超过 80% 的准确度预测农作物的干旱状态。