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

Abstract

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.

Object-Based Image Analysis Applied to Low Altitude Aerial Imagery for Potato Plant Trait Retrieval and Pathogen Detection

“…Only plants that were at least 2 full plants away from diseased instances (roughly 80 cm) were considered as candidates to ensure the same high degree of status certainty as the NAK. This mirrors the approach by [26] that used the same dataset to model potato disease.…”

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

“…The approach used in this study is capable of detecting C. sepium plants of various sizes. Compared to other studies [8,19,22] that used a hood or artificial lighting for image acquisition, our study targets weed detection under uncontrolled environments. It is difficult to directly compare the performance of the developed model as different datasets and metrics are used in different studies.…”

“…Pound et al [18] demonstrated that using deep learning can achieve state-of-the-art results (> 97% accuracy) for plant root and shoot identification and localization. Polder et al [19] adapted an fully convolutional neural network (FCN) for potato virus Y detection based on field hyperspectral images. Specifically, for crop/weed detection and segmentation, Sa et al [20,21] developed WeedNet and WeedMap architectures to analyse aerial images from an unmanned aerial vehicle (UAV) platform.…”

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

“…However, the distributions of the averaged NDVIs were largely overlapped. Figure 6a shows the estimated probability density distributions of the averaged NDVI for the two nitrogen treatments by using the kernel density estimate (KDE) [29]. In this study, the supervised machine learning analysis and figure plotting were performed in the Python version 3.7.2 software environment [45].…”

“…Due to the mechanisms of ML models, they can learn from data and, based on the learning, they can make the best predictions [23]. In fact, significant work has been done in similar plant phenotyping analyses, such as predicting nutrient contents [24,25], estimating the plant field [26], accessing biotic or abiotic stresses [27], and classifying desired traits [28,29]. However, analyses of the distribution features of leaves using supervised ML models has rarely been conducted.…”