A total of 288 (202 from potato and 86 from tomato) isolates of Phytophthora infestans were collected from 1998 to 2007 in China. The isolates were characterized based on mating type, in vitro metalaxyl sensitivity, virulence on potato differentials, allozymes of glucose-6-phosphate isomerase (Gpi), peptidase (Pep), and mitochondrial DNA (mtDNA) haplotype and examined by DNA-based simple sequence repeat (SSR) and random amplified polymorphic DNA (RAPD) fingerprinting. The majority (283 of 288) of the isolates were of the A1 mating type, the other three were the A2 mating type and two were the A1A2 mating type. Resistance to metalaxyl was frequently observed, with 248 (86.1%) resistant, 21 (7.3%) intermediate and 19 (6.6%) sensitive isolates identified. Virulence was assessed for 125 isolates on a set of 11 potato differentials and 61 races were detected. Most isolates were virulent on the differential genotype with gene R3, and all known virulence genes were found, with race 3.4.7.11 being the most common. This pattern did not appear to be associated with geographic origin, sample type, mating type or metalaxyl sensitivity. The dominant banding patterns for Gpi were 100 ⁄ 100 ⁄ 111 (176 isolates) and 100 ⁄ 100 (109 isolates), but genotypes 86 ⁄ 100 and 100 ⁄ 111 were also identified. All isolates tested were homozygous (100 ⁄ 100) at the Pep locus. The majority (205 of 288) of isolates tested was of mtDNA haplotype IIb, 76 were haplotype IIa and seven were the rare Ib haplotype. The genetic diversity of 60 representative isolates from China was assayed by two types of molecular markers, RAPD and SSR. A high level of polymorphism was found. The results demonstrated the diverse phenotypic and genotypic structure of the current populations of P. infestans in China.

Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, “red-nose” fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the ‘HLBdetector’ app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly ‘HLBdetector’ app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

International audienceIn order to improve the efficiency of dangerous and harmful species monitoring of import Taiwan fruits and vegetables and achieve informatization, networking and visualization of monitoring, this paper uses geography information system technology to develop monitoring system of import Taiwan fruits and vegetables dangerous and harmful species on the basis of the analysis of monitoring business needs. The system realizes some useful functions, such as showing monitoring data in map, map operation, monitoring data report and monitoring data management. This paper supply informatization supporting platform to dangerous and harmful species monitoring of import Taiwan fruits and vegetables and provide information support for scientific decision-making of alien invasive biological prevention and control department