Early, precise detection of nutrient deficiency stress (NDS) has key economic as well as environmental impact; precision application of chemicals in place of blanket application reduces operational costs for the growers while reducing the amount of chemicals which may enter the environment unnecessarily. Furthermore, earlier treatment reduces the amount of loss and therefore boosts crop production during a given season. With this in mind, we collect sequences of high-resolution aerial imagery and construct semantic segmentation models to detect and predict NDS across the field. Our work sits at the intersection of agriculture, remote sensing, and modern computer vision and deep learning. First, we establish a baseline for full-field detection of NDS and quantify the impact of pretraining, backbone architecture, input representation, and sampling strategy. We then quantify the amount of information available at different points in the season by building a single-timestamp model based on a UNet. Next, we construct our proposed spatiotemporal architecture, which combines a UNet with a convolutional LSTM layer, to accurately detect regions of the field showing NDS; this approach has an impressive IOU score of 0.53. Finally, we show that this architecture can be trained to predict regions of the field which are expected to show NDS in a later flight -- potentially more than three weeks in the future -- maintaining an IOU score of 0.47-0.51 depending on how far in advance the prediction is made. We will also release a dataset which we believe will benefit the computer vision, remote sensing, as well as agriculture fields. This work contributes to the recent developments in deep learning for remote sensing and agriculture, while addressing a key social challenge with implications for economics and sustainability.
翻译:早期准确检测营养素缺乏应激反应(NDS)具有重要的经济和环境影响;精确应用化学品取代全面应用,可以降低种植者的业务费用,同时减少不必要地进入环境的化学品数量;此外,早期处理可以减少损失数量,从而在特定季节促进作物生产;铭记这一点,我们收集高分辨率航空图像序列,并构建语义分解模型,以在实地探测和预测NDS。我们的工作处于农业、遥感和现代计算机愿景以及深层学习的交叉点;首先,我们为全方位检测NDS建立一个基线,并量化预培训、主干结构、输入和取样战略的影响;然后,我们通过在特定季节建立一个单一时间戳印模型来量化不同地点可获得的信息数量。接下来,我们建立我们拟议的超音频结构,将UNet与卷轴LSTM层结合起来,以准确探测显示NDS的实地挑战;首先,我们为全方位检测NDSDS的动态,并量化预培训前期、主干线结构的影响;最后,我们为未来几星期的飞行预言,我们预计未来农业将如何在远方进行远程评估。