High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field

Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.

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Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging.

A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping

https://www.mdpi.com/2079-6374/10/12/193/pdf

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Maize streak geminivirus (MSV) causes maize streak disease, a major disease limiting maize production over widespread areas of Africa. There has always been an urgency to develop quick and efficient methods of detecting such a disease for control purposes as well as increased food production and security. The use of remote sensing techniques for detecting the MSV infected maize was evaluated in this study based on experiments in Ofcolaco, Tzaneen, South Africa. Specifically, the potential of hyperspectral data for detecting different levels of MSV infection in maize was tested based on the guided regularized random forest (GRRF) algorithm. The findings of this study illustrate the strength of hyperspectral data in detecting different levels of MSV infection. Specifically, the optimal bands for detecting different levels of maize streak disease in maize were 552 nm, 603 nm, 683 nm, 881 nm and 2338 nm based on the GRRF algorithm. This study underscores the potential of remotely sensed data in the accurate d...

Detecting the severity of maize streak virus infestations in maize crop using in situ hyperspectral data

Cassava mosaic disease CMD is a prominent virus infection that causes considerable crop damage and yield reduction. Early detection of crop damage by remote sensing could be a useful tool for initiating remedial measures to reduce further crop damage. This article presents a non-destructive method for detection and classification of CMD infection, based on the red:far-red chlorophyll chl fluorescence image ratio. This pilot study was carried out in 14 varieties of potted cassava plants Manihotesculenta Crantz with a multispectral imaging system MSIS consisting of an electron multiplying charge coupled device EMCCD camera. Sunlight-induced chl fluorescence SICF images of plant leaves were recorded using the MSIS at the Fraunhofer lines of O2-B at 687 nm and O2-A at 759.5 nm and their off-lines at 684 and 757.5 nm. The recorded images were analysed using the Fraunhofer line discrimination FLD technique to extract the SICF from the solar reflectance in the recorded images. The chl fluorescence image ratio red:far-red, F687:F760 was computed and correlated with the laser-induced chl fluorescence LICF ratio F685:F735 determined by point monitoring, chl content variation, and the net photosynthetic rate Pn. The scatter plot of the F687:F760 image ratio showed good discrimination between different levels of CMD infection as evidenced by the high sensitivity and specificity values. It is observed that the fluorescence image ratio F687:F760 has a good correlation with Pn coefficient of determination R2 = 0.85, chl content R2 = 0.82, and the LICF ratio F685:F735R2 = 0.80, thereby highlighting the potential of the SICF image ratio in the discrimination of CMD infection. The results clearly indicate that changes in the red:far-red fluorescence image ratio due to CMD stress can easily be detected at an early stage and the technique has great potential for monitoring the health of crops and vegetation from proximal sensing platforms.

Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing

Cassava Mosaic virus Disease (CMD) is the most severe and widespread virus infection that affects cassava (Manihot esculenta Crantz) crops. This paper investigates the application of photochemical reflectance index (PRI) imaging to detect and assess the impact of varying levels of CMD infection in cassava. Towards this, narrow band reflectance images of field-grown cassava plants were recorded at 531 and 571 nm by proximal sensing with a multispectral imaging system (MSIS). It was observed that the PRI values increase with increasing levels of CMD infection in all the varieties of cassava studied. A scatter plot of the PRI image intensity yielded a sensitivity of 85 % and specificity of 79 % for discriminating visibly no CMD from initial CMD and a sensitivity of 93 % and specificity of 92 % for discriminating initial CMD from advanced CMD. Area under the receiver operator characteristics (AUC-ROC) curve was used to discriminate the CMD infection level by differentiating visibly no CMD from initial CMD [AUC = 0.92] and initial CMD from advanced CMD [AUC = 0.99]. It was observed that PRI values determined from the experimental data follow a linear inverse relationship with net photosynthetic rate (Pn) (R
 2 = 0.76) and total leaf chlorophyll (Chl) content (R
 2 = 0.80). The results show that PRI imaging can be utilized to discriminate healthy plants from CMD and other stress infected crops by proximal sensing in outdoor plants.

Sukumar Nita

Papers

Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing

Detection and Classification of Mosaic Virus Disease in Cassava Plants by Proximal Sensing of Photochemical Reflectance Index