Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images

Virus diseases are of high concern in the cultivation of seed potatoes. Once found inthe field, virus diseased plants lead to declassification or even rejection of the seed lotsresulting in a financial loss. Farmers put in a lot of effort to detect diseased plants andremove virus-diseased plants from the field. Nevertheless, dependent on the cultivar,virus diseased plants can be missed during visual observations in particular in an earlystage of cultivation. Therefore, there is a need for fast and objective disease detection.Early detection of diseased plants with modern vision techniques can significantlyreduce 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.