Cassava virus infection detected with handheld multispectral imager
Healthy cassava leaves (image courtesy of Pixabay)

One of the most important sources of carbohydrate energy in equatorial countries comes from the cassava plant. It looks like a trendy house plant but the roots are large tubers that provide valuable nutrition. Tubers can be boiled and mashed or dried, ground and turned into flour. Cassava is a robust crop but suffers from two virus diseases that can ruin an entire crop with very little warning until the tubers are dug up at harvest. Rotten tubers cannot be used for food and signs of viral infection are not obvious on the leaves or stems until it is too late to plant a replacement crop. Propagation of cuttings for the following year’s crop is also affected by virus so potentially two years worth of food could be lost in a single infection.

Cassava mosaic virus (CMV) and cassava brown streak disease (CBSD) are the two main viral diseases responsible for crop loss. There are now cassava varieties that are resistant to CMV but CBSD is still problematic. There are various biochemical diagnostic tests for the diseases but the most reliable, a PCR test, is expensive, invasive and requires a relatively high level of viral load. Prompted by the need for a better, quicker test a team of researchers from University of Manchester, North Carolina State University, Rutgers University and the International Institute of Tropical Agriculture have developed a handheld multispectral leaf imager that detects the presence of CBSD before signs are obvious to the human eye.

Cassava tubers ready for processing (image courtesy of PixaBay)

In a pre-print paper last month, Hujun Yin and colleagues reported how a compact 14 wavelength multispectral leaf imaging device utilising machine learning successfully classified diseased plants and control plants. Photos of the device are shown below.

Multispectral leaf imager (a) and sample chamber window with grid to hold leaf flat and LED ring for illumination (b) (image courtesy of Creative Commons, Research Square and the authors)

The Manchester study comprised three trials, each containing cassava plants naturally immune to CMV to minimise the chance of random viral infection from another source. All three trials had three treatment groups: controls; CBSD inoculated; and E. coli inoculated. The last treatment groups were used to test the susceptibility of the inoculation method itself, E. coli should have no effect on the health of the cassava plants. Plants were measured at days 7, 14, 21, 28, 52, 59 and 88 days post inoculation (dpi). Leaf images were recorded using 14 different LED light wavelengths (395, 415, 470, 528, 532, 550, 570, 585, 590, 610, 625, 640, 660, 700 and 880 nm). At each time point plant leaves were also given scores from 1 to 4 based on how they appeared to the eye. Typical leaves are shown below.

Cassava leaf scores (1-4) at days post inoculation

In trial 2, PCR tests were performed on leaves at each time point and visual scores were documented. The visual scores showed a progression with time for the inoculated leaves as expected (see below).

Cassava leaf scores as trial 2 progressed

To verify that the virus was indeed successfully inoculated into the plants, PCR tests confirmed the virus present in some plant leaves after day 52, with the highest levels at the end of the time course.

Analysis of the leaf image spectral hypercubes (14 wavelengths x 12 random groups of leaf pixels) produced metadata consisting of six vegetation indices (VIs); average spectral intensities (ASIs) and texture (a measure of the variation of the intensities from pixel group to pixel group on a leaf). Interestingly, even some simple VIs were capable of distinguishing some diseased plants from healthy ones (more than 60% successful classification at day 52, comparable to the PCR testing).

Metadata was used to create a classifier based on measurements taken from plants with positive PCR test results. Rather than use a convolutional neural network (CNN) approach to classify the images or metadata, the team used a Support Vector Machine (SVM). SVMs have the advantage that they typically do not require high computing power and they can be intuitively quantitative using simple regression to find the best dividing lines between image categories. SVM produced a marked improvement in classification with better than 80% success as a result of using positional information in addition to VI. An introductory reference is given at the end of this post.

The group made one further improvement to their model and this was to combine subsets of classifiers produced by the SVM. They called this approach Decision Fusion (DF) and Probabilistic Decision Fusion (PDF), which is basically saying if one classifier doesn’t work, combine it with another and see if the performance improves. Finally they achieved sophisticated classification of diseased and healthy plants at day 53 and 80-90% success.

It will be very interesting to see multispectral imaging applied to more diseases and to the classification of differing diseases.

The pre-print paper can be found here, courtesy of Research Square and the authors Yao Peng, Mary Dallas, José T. Ascencio-Ibáñez, Steen Hoyer, James Legg, Linda Hanley-Bowdoin, Bruce Grieve, Hujun Yin.

A nice introduction to Support Vector Machines from MonkeyLearn can be found here.

Information on our own BACO multispectral leaf imager can be found here.