Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images
Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images
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DOI：
10.3389/fpls.2019.00209
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发表时间：
2019-03-01
影响因子：
5.6
通讯作者：
Kamp, Jan
中科院分区：
文献类型：
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作者：
Polder, Gerrit;Blok, Pieter M.;Kamp, Jan
病毒病是马铃薯种薯栽培中的重要病害。一旦在田间发现，病毒病植物导致解密甚至拒绝种子批次，造成经济损失。农民投入了大量的精力来检测病株并将病毒病株从田地中清除。然而，取决于栽培品种，在视觉观察期间，特别是在栽培的早期阶段，可能会错过病毒病植物。因此，需要快速和客观的疾病检测。利用现代视觉技术早期检测患病植物可以显著降低成本。前几年的实验室实验表明，高光谱成像可以清楚地区分健康的马铃薯植物和感染病毒的马铃薯植物。本文报道了我们的第一个真实的现场实验。设计了一种新的成像装置，包括一个高光谱线扫描相机。高光谱图像在现场拍摄的线间隔为5毫米。一个完全卷积神经网络适用于高光谱图像，并在现场的两个实验行进行训练。训练好的网络在另外两行上用不同的马铃薯品种进行验证。对于四个行/日期组合中的三个，与常规疾病评估相比，精确度和召回率分别超过0.78和0.88。这证明了该方法对于真实的世界疾病检测的适用性。
Virus diseases are of high concern in the cultivation of seed potatoes. Once found in the field, virus diseased plants lead to declassification or even rejection of the seed lots resulting in a financial loss. Farmers put in a lot of effort to detect diseased plants and remove virus-diseased plants from the field. Nevertheless, dependent on the cultivar, virus diseased plants can be missed during visual observations in particular in an early stage of cultivation. Therefore, there is a need for fast and objective disease detection. Early detection of diseased plants with modern vision techniques can significantly reduce costs. Laboratory experiments in previous years showed that hyperspectral imaging clearly could distinguish healthy from virus infected potato plants. This paper reports on our first real field experiment. A new imaging setup was designed, consisting of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a line interval of 5 mm. A fully convolutional neural network was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. For three of the four row/date combinations the precision and recall compared to conventional disease assessment exceeded 0.78 and 0.88, respectively. This proves the suitability of this method for real world disease detection.