Many species of the genus Bipolaris are important plant pathogens and often cause leaf spot, root rot, and seedling blight in an extremely wide range of hosts around the world. In recent years, maize leaf spot caused by Bipolaris species has frequently occurred with complex symptoms and is becoming increasingly serious in Sichuan Province of China. To investigate the population diversity of Bipolaris spp. and their corresponding symptoms in maize, 747 samples of maize leaf spot were collected from 132 sampling sites in 19 administrative districts of Sichuan Province from 2011 to 2018. Based on morphological characteristics, pathogenicity testing, and phylogenetic analysis of the rDNA internal transcribed spacer (ITS) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) genes, a total of 1186 Bipolaris isolates were identified as B. maydis, B. zeicola, B. cynodontis, B. oryzae, B. setariae, and B. saccharicola, among which B. maydis and B. zeicola were the dominant pathogenic species, accounting for 57.34% and 42.07% of the isolates, respectively. We found that B. zeicola isolates were mainly distributed in high altitude and cool mountainous areas, while B. maydis was more widely distributed in Sichuan Province. The typical symptoms caused by the Bipolaris species were clearly distinct in maize. The typical symptoms caused by B. maydis were elongated strip lesions, or fusiform, elliptical lesions, and those caused by B. zeicola were narrow linear lesions. Herein, B. saccharicola was first reported on maize and caused subrotund lesions. This study provides useful information for disease diagnosis and management for Bipolaris leaf spot in maize.

The prevention and management of crop diseases play an important role in agricultural production, but there are many types of crop diseases and complex causes, and their prevention and identification add difficulties to the process. The traditional methods of identifying diseases mostly rely on human visual and manual inspection, which requires a certain amount of expert knowledge and experience. There are shortcomings such as strong subjectivity and low accuracy. This paper takes the common diseases of kiwifruit as the research object. Based on deep learning and computer vision models, and given the influence of a complex background in actual scenes on the detection of diseases, as well as the shape and size characteristics of diseases, an innovative method of target detection and semantic segmentation was proposed to identify diseases accurately. The main contributions of this research are as follows: We produced the world’s first high-quality dataset on kiwifruit. We used the target detection algorithm YOLOX, we stripped the kiwi leaves from the natural background and removed the influencing factors existing in the complex background. Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. We used the training method of learning rate decay to further improve the training effect without increasing the training cost. After experimental verification, our two-stage disease detection algorithm had the advantages of high accuracy, strong robustness, and wide detection range, which provided a more efficient solution for solving the problem of precise monitoring of crop growth environment parameters.

Anthracnose caused by Colletotrichum spp. is an important disease of blueberries and results in large economic losses for blueberry growers. Samples of anthracnose were collected from six main blueberry cultivation areas in Sichuan Province. In total, 74 Colletotrichum isolates were obtained through a single-spore purification method and identified to the species through morphological characteristics and phylogenetic analyses based on partial DNA sequences of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), internal transcribed spacer (ITS) regions, and the β-tubulin (TUB2), actin (ACT) and calmodulin (CAL) genes. Among all species, Colletotrichum fructicola was the most dominant species, with an isolation percentage of up to 66.2% in Sichuan, followed by Colletotrichum siamense (17.6%), C. kahawae (5.4%), C. karstii (5.4%), C. nymphaeae (2.7%) and C. sichuaninese (2.7%). Pathogenicity tests showed all species were able to cause typical anthracnose symptoms on blueberry leaves and stems. Colletotrichum fructicola was the predominant species with strong aggressiveness. Moreover, C. fructicola, C. kahawae, C. sichuaninese and C. nymphaeae are first reported here to cause blueberry anthracnose. This study provides a comprehensive reference for the association of different Colletotrichum spp., which may support the sustainable management of blueberry anthracnose.

The ecology of soil fungi is poorly understood, and recent comprehensive reports on Trichoderma are unavailable for any region, including the Zoige alpine wetland ecological region in China. One hundred soil samples were collected from different soil types and soil layers in Zoige alpine wetland ecological regions. Using the traditional suspension plating method, 80 Trichoderma strains were chosen to analyze species diversity. After a preliminary classification of morphological characteristics and the genes glyceraldehyde-3-phosphate dehydrogenase (gpd), 57 representative strains were selected and eventually identified as seven species via phylogenetic analyses of multilocus sequences based on the genes transcription elongation factor 1 alpha (tef1), encoding RNA polymerase II subunit B (rpb2) and ATP citrate lyase (acl1). Among them, T. harzianum was the dominant species isolated from five soil layers and four soil types, and had the highest isolation frequency (23%) in this zone, while T. polysporum and T. pyramidale were rare species, with isolation frequencies of less than 1%. Our detailed morphological observation and molecular phylogenetic analyses support the recognition of Trichoderma zoigense was described for the first time as a new species, while T. atrobrunneum as a new record for China was found. Our results will be used as a reference for a greater understanding of soil microbial resources, ecological rehabilitation and reconstructions in the Zoige alpine wetland.

Disease diagnosis and control play important roles in agriculture and crop protection. Traditional methods of identifying plant disease rely primarily on human vision and manual inspection, which are subjective, have low accuracy, and make it difficult to estimate the situation in real time. At present, an intelligent detection technology based on computer vision is becoming an increasingly important tool used to monitor and control crop disease. However, the use of this technology often requires the collection of a substantial amount of specialized data in advance. Due to the seasonality and uncertainty of many crop pathogeneses, as well as some rare diseases or rare species, such data requirements are difficult to meet, leading to difficulties in achieving high levels of detection accuracy. Here, we use kiwifruit trunk bacterial canker (Pseudomonas syringae pv. actinidiae) as an example and propose a high-precision detection method to address the issue mentioned above. We introduce a lightweight and efficient image generative model capable of generating realistic and diverse images of kiwifruit trunk disease and expanding the original dataset. We also utilize the YOLOv8 model to perform disease detection; this model demonstrates real-time detection capability, taking only 0.01 s per image. The specific contributions of this study are as follows: (1) a depth-wise separable convolution is utilized to replace part of ordinary convolutions and introduce noise to improve the diversity of the generated images; (2) we propose the GASLE module by embedding a GAM, adjust the importance of different channels, and reduce the loss of spatial information; (3) we use an AdaMod optimizer to increase the convergence of the network; and (4) we select a real-time YOLOv8 model to perform effect verification. The results of this experiment show that the Fréchet Inception Distance (FID) of the proposed generative model reaches 84.18, having a decrease of 41.23 compared to FastGAN and a decrease of 2.1 compared to ProjectedGAN. The mean Average Precision (mAP@0.5) on the YOLOv8 network reaches 87.17%, which is nearly 17% higher than that of the original algorithm. These results substantiate the effectiveness of our generative model, providing a robust strategy for image generation and disease detection in plant kingdoms.

Pseudomonas syringae pv. actinidiae (Psa) is responsible for causing kiwifruit canker disease. The detection of Psa is commonly carried out using normal PCR and culture-based isolation. However, normal PCR does not differentiate between live and dead cells, potentially resulting in the incorrect estimation of the amount of infectious substance in a sample. Such an incorrect estimation could result in unnecessary phytosanitary strategies and control measures. This study attempts to establish a specific assay for detecting only live Psa bacterial cells. To achieve this, a pair of strain-specific primers designed from HopZ3 effector were used, and the traditional PCR method was assessed using a nucleic acid-binding dye (propidium monoazide—PMA), establishing a PMA–PCR system and conditions for detecting live Psa in this study. Sensitivity tests showed a detection limit of 10 cfu/mL and 1 pg/μL. This method was also tested in diseased kiwifruit tissues and can be seen as a rapid and dependable replacement to PCR methods for detecting only those infective kiwifruit materials with viable Psa.