Grain rot dataset caused by Burkholderia Glumae Bacteria

The Burkholderia glumae bacterium causes bacterial grain rot in rice, posing significant threats to the crop's yield, particularly thriving during the rice flowering and grain filling stages. This disease is especially evident in rice grains before harvest, presenting challenges in the detection and classification of rice panicles. Firstly, diseased grains may mix with healthy ones, complicating their separation. Secondly, the size of grains on a panicle varies from small to large, which can be problematic when detected using object detection methods. Thirdly, disease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield. Finally, the challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods and support smart agriculture.

Rice pest dataset supports the construction of smart farming systems

Rice holds a significant position in the global food supply chain, particularly in Asian, African, and Latin American countries. However, rice pests and diseases cause significant damage to the supply and growth of the rice cultivation industry. Therefore, this article provides a high-quality dataset that has been reviewed by agricultural experts. The dataset is well-suited to support the development of automation systems and smart farming practices. It plays a vital role in facilitating the automatic construction, detection, and classification of rice diseases. However, challenges arise due to the diversity of the dataset collected from various sources, varying in terms of disease types and sizes. This necessitates support for upgrading and enhancing the dataset through various operations in data processing, preprocessing, and statistical analysis. The dataset is provided completely free of charge and has been rigorously evaluated by agricultural experts, making it a reliable resource for system development, research, and communication needs.

Predicting rice diseases using advanced technologies at different scales: present status and future perspectives

The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.

Diagnosis and application of rice diseases based on deep learning

Background

Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some current studies are based on images with simple backgrounds, while realistic scene settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods not only have heavy workload, low efficiency, but are also haphazard, unable to achieve real-time monitoring, which seriously limits the development of modern agriculture. Therefore, using target detection algorithm to identify rice diseases is an important research direction in the agricultural field.

Methods

In this article a total of 7,220 pictures of rice diseases taken in Jinzhai County, Lu'an City, Anhui Province were chosen as the research object, including rice leaf blast, bacterial blight and flax leaf spot. We propose a rice disease identification method based on the improved YOLOV5s, which reduces the computation of the backbone network, reduces the weight file of the model to 3.2MB, which is about 1/4 of the original model, and accelerates the prediction speed by three times.

Results

Compared with other mainstream methods, our method achieves better performance with low computational cost. It solves the problem of slow recognition speed due to the large weight file and calculation amount of model when the model is deployed in mobile terminal.

First report of Fusarium incarnatum causing spikelet rot on rice in China

Rice (Oryza sativa L.) is the most important and widely grown crop, covering about 29.9 million ha of total cultivation area in China. In the last decade, spikelet rot disease on rice became much more frequent in the middle and lower reaches of the Yangtze River, China. Fusarium proliferatum (Matsush.) Nirenberg ex Gerlach & Nirenberg was reported to be a causal agent of spikelet rot on rice in Hangzhou, Zhejiang province (Huang et al. 2012). In September 2019, a survey was conducted to understand the etiology of the disease in the main rice growing regions of Jinshan District of Shanghai. Symptomatic panicles exhibiting reddish or brown discoloration on the glumes were collected from different rice fields, where disease incidence was estimated to be between 20 to 80%. Diseased glumes were cut into small sections (5 × 5 mm) from the boundary of necrotic and healthy tissues, surface-sterilized with 75% ethanol for 30 s and 3% sodium hypochlorite for 90 s, rinsed twice with sterile distilled water, then placed onto 1/5 strength potato dextrose agar (PDA). After 3 to 5 days of incubation at 28°C in the dark, fungal growth with Fusarium-like colonies were transferred to PDA and purified by the single-spore isolation method. A total of 12 isolates were obtained and colonies showed loosely floccose, white mycelium and pale-yellow pigmentation on PDA. Microconidia were ovoid mostly with 0 to 1 septum, and measured 4.2 to 16.6 × 2.5 to 4.1 μm (n = 50). After 5-7 days of inoculation on carnation leaf agar (CLA), macroconidia produced usually had 3 to 5 septa, slightly curved at the apex, ranging from 15.7 to 39.1 × 3.3 to 5.0 μm (n = 50). Chlamydospores were produced in hyphae, most often solitary in short chains or in clumps, ellipsoidal or subglobose with thick and roughened walls. Molecular identification was performed on the representative isolates (JS3, JS9, and JS21). The rDNA internal transcribed spacer (ITS), translation elongation factor (TEF-1α) and β-tubulin (β-TUB) genes were amplified and sequenced using the paired primers ITS1/ITS4 (White et al. 1990), EF1/EF2 (O'Donnell et al. 1998) and T1/T22 (O'Donnell and Cigelnik 1997), respectively. The obtained sequences were deposited in GenBank under accession numbers MT889972 to MT889974 (ITS), MT895844 to MT895846 (TEF-1α), and MT895841 to MT895843 (β-TUB), respectively. BLASTn search of the sequences revealed 99 to 100% identity with ITS (MF356578), TEF-1α (HM770725) and β-TUB (GQ915444) of Fusarium incarnatum isolates. FUSARIUM-ID (Geiser et al. 2004) analysis showed 99 to 100% similarity with sequences of the F. incarnatum-equiseti species complex (FIESC) (FD_01651 and FD_01628). In addition, a phylogenetic analysis based on the concatenated nucleotide sequences placed the isolates in the F. incarnatum clade at 100% bootstrap support. Thus, both morphological observations and molecular criteria supported identification of the isolates as F. incarnatum (Desm.) Sacc (synonym: Fusarium semitectum) (Leslie and Summerell 2006, Nirenberg 1990). Pathogenicity tests were performed on susceptible rice cultivar 'Xiushui134'. At pollen cell maturity stage, a 2-ml conidial suspension (5 × 105 macroconidia/ml) of each isolate was injected into 10 rice panicles. Control plants were inoculated with sterile distilled water. Then, the pots were kept in a growth chamber at 28°C, 80% relative humidity, and 12 h/12 h light (10,000 lux)/dark. The experiment was repeated two times for each isolate. Two weeks post-inoculation, all inoculated panicles showed similar symptoms with the original samples, whereas no symptoms were observed on the control. The pathogen was re-isolated from inoculated panicles and identified by the method described above to fulfill Koch's postulates. Previous studies reported that F. incarnatum reproduced perithecia to overwinter on rice stubble as the inoculum of Fusarium head blight of wheat in southern China (Yang et al. 2018). To our knowledge, this is the first report of spikelet rot on rice caused by F. incarnatum in China. Further investigation is needed to gain a better understanding its potential geographic distribution of this new pathogen on rice crop. References: (1) Huang, S. W., et al. 2011. Crop Prot. 30: 10. (2) White, T. J., et al. 1990. Page 315 in: PCR Protocols: A Guide to Methods and Applications. Academic Press, San Diego, CA. (3) O'Donnell, K., et al. 1998. Proc. Natl. Acad. Sci. U.S.A. 95: 2044. (4) O'Donnell, K., Cigelnik, E. 1997. Mol. Phylogenet. Evol. 7: 103. (5) Geiser, D. M., et al. 2004. Eur. J. Plant Pathol. 110: 473. (6) Leslie, J. F., and Summerell, B. A. 2006. The Fusarium Laboratory Manual. Blackwell, Ames, IA. (7) Nirenberg, H. I. 1990. Stud. Mycol. 32: 91. (8) Yang, M. X., et al. 2018. Toxins. 10: 115. The author(s) declare no conflict of interest. Funding: Funding was provided by National Natural Science Foundation of China (grant no. 31800133), Zhejiang Provincial Natural Science Foundation of China (grant no. LQ18C140005), Key Research and Development Program of Zhejiang Province (grant no. 2019C02018), Shanghai Science and Technology for Agriculture Promotion Project (2019-02-08-00-08-F01127), and the Agricultural Science and Technology Innovation Program of China Academy of Agricultural Science (CAAS-ASTIP-2013- CNRRI).