Early detection of Psa infection in kiwifruit by means of infrared thermography at leaf and orchard scale

Scientific and technological advances in the development of sustainable disease management tools: a case study on kiwifruit bacterial canker

Plant disease outbreaks are increasing in a world facing climate change and globalized markets, representing a serious threat to food security. Kiwifruit Bacterial Canker (KBC), caused by the bacterium Pseudomonas syringae pv. actinidiae (Psa), was selected as a case study for being an example of a pandemic disease that severely impacted crop production, leading to huge economic losses, and for the effort that has been made to control this disease. This review provides an in-depth and critical analysis on the scientific progress made for developing alternative tools for sustainable KBC management. Their status in terms of technological maturity is discussed and a set of opportunities and threats are also presented. The gradual replacement of susceptible kiwifruit cultivars, with more tolerant ones, significantly reduced KBC incidence and was a major milestone for Psa containment - which highlights the importance of plant breeding. Nonetheless, this is a very laborious process. Moreover, the potential threat of Psa evolving to more virulent biovars, or resistant lineages to existing control methods, strengthens the need of keep on exploring effective and more environmentally friendly tools for KBC management. Currently, plant elicitors and beneficial fungi and bacteria are already being used in the field with some degree of success. Precision agriculture technologies, for improving early disease detection and preventing pathogen dispersal, are also being developed and optimized. These include hyperspectral technologies and forecast models for Psa risk assessment, with the latter being slightly more advanced in terms of technological maturity. Additionally, plant protection products based on innovative formulations with molecules with antibacterial activity against Psa (e.g., essential oils, phages and antimicrobial peptides) have been validated primarily in laboratory trials and with few compounds already reaching field application. The lessons learned with this pandemic disease, and the acquired scientific and technological knowledge, can be of importance for sustainably managing other plant diseases and handling future pandemic outbreaks.

The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications

The dynamic evolution of photogrammetry led to the development of numerous methods of geometric calibration of cameras, which are mostly based on building flat targets (fields) with features that can be distinguished in the images. Geometric calibration of thermal cameras for UAVs is an active research field that attracts numerous researchers. As a result of their low price and general availability, non-metric cameras are being increasingly used for measurement purposes. Apart from resolution, non-metric sensors do not have any other known parameters. The commonly applied process is self-calibration, which enables the determining of the approximate elements of the camera’s interior orientation. The purpose of this work was to analyze the possibilities of geometric calibration of thermal UAV cameras using proposed test field patterns and materials. The experiment was conducted on a FLIR VUE PRO thermal camera dedicated to UAV platforms. The authors propose the selection of various image processing methods (histogram equalization, thresholding, brightness correction) in order to improve the quality of the thermograms. The consecutive processing methods resulted in over 80% effectiveness on average by 94%, 81%, and 80 %, respectively. This effectiveness, for no processing and processing with the use of the filtering method, was: 42% and 38%, respectively. Only high-pass filtering did not improve the obtained results. The final results of the proposed method and structure of test fields were verified on chosen geometric calibration algorithms. The results of fast and low-cost calibration are satisfactory, especially in terms of the automation of this process. For geometric correction, the standard deviations for the results of specific methods of thermogram sharpness enhancement are two to three times better than results without any correction.

Thermal Imaging for Plant Stress Detection and Phenotyping

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season’s first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit.

A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors

Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a Xanthomonas strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.

Detection of Gray Mold Leaf Infections Prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor

Fungal leaf diseases cause economically important damage to crop plants. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. Therefore, early disease detection prior symptom development would allow an earlier, and therefore more effective, curative management of fungal diseases. Using a five-lens multispectral imager, spectral reflectance of green, blue, red, near infrared (NIR, 840 nm), and rededge (RE, 720 nm) was recorded in time-course experiments of detached tomato leaves inoculated with the fungus Botrytis cinerea and mock infection solution. Linear regression models demonstrate NIR and RE as the two most informative spectral data sets to differentiate pathogen- and mock-inoculated leaf regions of interest (ROI). Under controlled laboratory conditions, bands collecting NIR and RE irradiance showed a lower reflectance intensity of infected tomato leaf tissue when compared with mock-inoculated leaves. Blue and red channels collected higher intensity values in pathogen- than in mock-inoculated ROIs. The reflectance intensities of the green band were not distinguishable between pathogen- and mock infected ROIs. Predictions of linear regressions indicated that gray mold leaf infections could be identified at the earliest at 9 h post infection (hpi) in the most informative bands NIR and RE. Re-analysis of the imagery taken with NIR and RE band allowed to classify infected tissue.

Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture

Remote sensing with unmanned aerial vehicles (UAVs) is a game-changer in precision agriculture. It offers unprecedented spectral, spatial, and temporal resolution, but can also provide detailed vegetation height data and multiangular observations. In this article, we review the progress of remote sensing with UAVs in drought stress, in weed and pathogen detection, in nutrient status and growth vigor assessment, and in yield prediction. To transfer this knowledge to everyday practice of precision agriculture, future research should focus on exploiting the complementarity of hyperspectral or multispectral data with thermal data, on integrating observations into robust transfer or growth models rather than linear regression models, and on combining UAV products with other spatially explicit information.

Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?

Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts.

Picturing pathogen infection in plants

Several imaging techniques have provided valuable tools to evaluate the impact of biotic stress on host plants. The use of these techniques enables the study of plant-pathogen interactions by analysing the spatial and temporal heterogeneity of foliar metabolism during pathogenesis. In this work we review the use of imaging techniques based on chlorophyll fluorescence, multicolour fluorescence and thermography for the study of virus, bacteria and fungi-infected plants. These studies have revealed the impact of pathogen challenge on photosynthetic performance, secondary metabolism, as well as leaf transpiration as a promising tool for field and greenhouse management of diseases. Images of standard chlorophyll fluorescence (Chl-F) parameters obtained during Chl-F induction kinetics related to photochemical processes and those involved in energy dissipation, could be good stress indicators to monitor pathogenesis. Changes on UV-induced blue (F440) and green fluorescence (F520) measured by multicolour fluorescence imaging in pathogen-challenged plants seem to be related with the up-regulation of the plant secondary metabolism and with an increase in phenolic compounds involved in plant defence, such as scopoletin, chlorogenic or ferulic acids. Thermal imaging visualizes the leaf transpiration map during pathogenesis and emphasizes the key role of stomata on innate plant immunity. Using several imaging techniques in parallel could allow obtaining disease signatures for a specific pathogen. These techniques have also turned out to be very useful for presymptomatic pathogen detection, and powerful non-destructive tools for precision agriculture. Their applicability at lab-scale, in the field by remote sensing, and in high-throughput plant phenotyping, makes them particularly useful. Thermal sensors are widely used in crop fields to detect early changes in leaf transpiration induced by both air-borne and soil-borne pathogens. The limitations of measuring photosynthesis by Chl-F at the canopy level are being solved, while the use of multispectral fluorescence imaging is very challenging due to the type of light excitation that is used.

Soil moisture controls on phenology and productivity in a semi-arid critical zone

The Earth's Critical Zone, where physical, chemical and biological systems interact, extends from the top of the canopy to the underlying bedrock. In this study, we investigated soil moisture controls on phenology and productivity of an Acacia woodland in semi-arid central Australia. Situated on an extensive sand plain with negligible runoff and drainage, the carry-over of soil moisture content (θ) in the rhizosphere enabled the delay of phenology and productivity across seasons, until conditions were favourable for transpiration of that water to prevent overheating in the canopy. Storage of soil moisture near the surface (in the top few metres) was promoted by a siliceous hardpan. Pulsed recharge of θ above the hardpan was rapid and depended upon precipitation amount: 150mm storm(-1) resulted in saturation of θ above the hardpan (i.e., formation of a temporary, discontinuous perched aquifer above the hardpan in unconsolidated soil) and immediate carbon uptake by the vegetation. During dry and inter-storm periods, we inferred the presence of hydraulic lift from soil storage above the hardpan to the surface due to (i) regular daily drawdown of θ in the reservoir that accumulates above the hardpan in the absence of drainage and evapotranspiration; (ii) the dimorphic root distribution wherein most roots were found in dry soil near the surface, but with significant root just above the hardpan; and (iii) synchronisation of phenology amongst trees and grasses in the dry season. We propose that hydraulic redistribution provides a small amount of moisture that maintains functioning of the shallow roots during long periods when the surface soil layer was dry, thereby enabling Mulga to maintain physiological activity without diminishing phenological and physiological responses to precipitation when conditions were favourable to promote canopy cooling.