Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques
Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques
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DOI：
10.1109/mitp.2020.2986103
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发表时间：
2020-05-01
期刊：
影响因子：
2.6
通讯作者：
Lobaton, Edgar
中科院分区：
文献类型：
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作者：
Ramos-Giraldo, Paula;Reberg-Horton, Chris;Lobaton, Edgar
干旱胁迫的实时检测对于防止由于多变的天气条件和持续的气候变化而造成的经济作物产量损失具有重大意义。玉米和大豆干旱敏感性/耐受性最广泛使用的指标是水分胁迫期间叶片是否枯萎。我们开发了一种低成本的自动化干旱检测系统，使用计算机视觉和机器学习（ML）算法来记录玉米和大豆大田作物的干旱响应。使用机器学习，我们预测作物的干旱状况，相对于专家得出的视觉干旱评级，准确率超过80%。
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.