Engineering fire blight resistance into the apple cultivar 'Gala' using the FB_MR5 CC-NBS-LRR resistance gene of Malus × robusta 5.

The fire blight susceptible apple cultivar Malus × domestica Borkh. cv. 'Gala' was transformed with the candidate fire blight resistance gene FB_MR5 originating from the crab apple accession Malus × robusta 5 (Mr5). A total of five different transgenic lines were obtained. All transgenic lines were shown to be stably transformed and originate from different transgenic events. The transgenic lines express the FB_MR5 either driven by the constitutive CaMV 35S promoter and the ocs terminator or by its native promoter and terminator sequences. Phenotyping experiments were performed with Mr5-virulent and Mr5-avirulent strains of Erwinia amylovora, the causal agent of fire blight. Significantly less disease symptoms were detected on transgenic lines after inoculation with two different Mr5-avirulent E. amylovora strains, while significantly more shoot necrosis was observed after inoculation with the Mr5-virulent mutant strain ZYRKD3_1. The results of these experiments demonstrated the ability of a single gene isolated from the native gene pool of apple to protect a susceptible cultivar from fire blight. Furthermore, this gene is confirmed to be the resistance determinant of Mr5 as the transformed lines undergo the same gene-for-gene interaction in the host-pathogen relationship Mr5-E. amylovora.

Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring.

BACKGROUND: The fruit fly Drosophila suzukii, or spotted wing drosophila (SWD), is a serious pest worldwide, attacking many soft-skinned fruits. An efficient monitoring system that identifies and counts SWD in crops and their surroundings is therefore essential for integrated pest management (IPM) strategies. Existing methods, such as catching flies in liquid bait traps and counting them manually, are costly, time-consuming and labour-intensive. To overcome these limitations, we studied insect trap monitoring using image-based object detection with deep learning. RESULTS: Based on an image database with 4753 annotated SWD flies, we trained a ResNet-18-based deep convolutional neural network to detect and count SWD, including sex prediction and discrimination. The results show that SWD can be detected with an area under the precision recall curve (AUC) of 0.506 (female) and 0.603 (male) in digital images taken from a static position. For images collected using an unmanned aerial vehicle (UAV), the algorithm detected SWD individuals with an AUC of 0.086 (female) and 0.284 (male). The lower AUC for the aerial imagery was due to lower image quality caused by stabilisation manoeuvres of the UAV during image collection. CONCLUSION: Our results indicate that it is possible to monitor SWD using deep learning and object detection. Moreover, the results demonstrate the potential of UAVs to monitor insect traps, which could be valuable in the development of autonomous insect monitoring systems and IPM. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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.

The Distance Between Forests and Crops Affects the Abundance of Drosophila suzukii During Fruit Ripening, But Not During Harvest.

Drosophila suzukii (Matsumura; Diptera: Drosophilidae) is an invasive pest with the ability to reproduce not only in various soft fruit crops, but also in numerous wild hosts. Forests and forest edges harbor many wild hosts, provide suitable microclimatic conditions and are therefore thought to enhance the abundance of D. suzukii. Although the comprehension of pest activity based on specific landscape elements is important to implement efficient management strategies, knowledge of how forests affect the abundance of D. suzukii in nearby crops is very limited. We conducted a monitoring study with liquid baited traps across different crops at different distance from the forests. During fruit ripening, more flies were captured in crops closer to forests (22.21 % decrease per 500 m distance), whereas there was no significant relationship during harvest. Since color can affect the efficiency of D. suzukii traps, we have used traps either with a red or black lid. Acquired data suggest that traps with black lids capture significantly more flies than traps with red lids. We provide a quantitative estimation of how and when distance from adjacent forests affects the abundance of D. suzukii in crop fields. Our results can help consultants and farmers to estimate the pest pressure of D. suzukii in crop fields near forested, noncrop areas and to implement appropriate control strategies when D. suzukii populations increase and fruit becomes susceptible to infestation.

Pathogen recognition in compatible plant-microbe interactions.

Microbial infections in plant leaves remain a major challenge in agriculture. Hence an understanding of disease mechanisms at the molecular level is of paramount importance for identifying possible intervention points for their control. Whole-transcriptome changes during early disease stages in susceptible plant species are less well-documented than those of resistant ones. This study focuses on the differential transcriptional changes at 24 hours post inoculation (hpi) in tomato leaflets affected by three pathogens: (1) Phytophthora infestans, (2) Botrytis cinerea, and (3) Oidium neolycopersici. Grey mould (B. cinerea) was the disease that had progressed the most by 24 hpi, both in terms of visible symptoms as well as differential gene expression. By means of RNA-seq, we identified 50 differentially expressed tomato genes specifically induced by B. cinerea infection and 18 specifically induced by P. infestans infection at 24 hpi. Additionally, a set of 63 genes were differentially expressed during all three diseases when compared by a Bayesian approach to their respective mock infections. And Gene expression patterns were found to also depend on the inoculation technique. These findings suggest a specific and distinct transcriptional response in plant leaf tissue in reaction to B. cinerea and P. infestans invasion at 24 hpi, indicating that plants may recognize the attacking pathogen.