Applications in Plant Sciences最新文献

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.

Morris, A. B., C. Scalf, A. Burleyson, L. T. Johnson, and K. Trostel. 2016. Development and characterization of microsatellite primers in the federally endangered Astragalus bibullatus (Fabaceae). Applications in Plant Sciences 4(4): e1500126.

In Table 1, the reverse primer sequences for loci Abib051, Abib156, and Abib170 were incorrect. Table 1 is provided here with the corrected sequences shown in boldface.

We apologize for this error.

Coal balls, in which fossil plants are preserved in permineralized peat deposits, have widely been described from coal deposits representing the tropical forest of the Carboniferous. Coal ball preparation techniques have evolved over the past century, with the cellulose acetate peel method becoming the standard in the 1950s. While coal ball research is not as active as it has been in the past, large collections of coal balls and their respective peels still form a large part of many museum and university collections. This contribution aims to review coal ball preparation methods, curation, and the digital archiving of peels to create a cohesive guide for researchers working with coal balls and other petrified plant material. The physical and digital curation of cellulose acetate peels and other types of coal ball specimens is critical for long-term preservation and accessibility. Physical curation involves embedding coal balls in media to slow pyrite deterioration. Digital curation creates high-resolution scans of peels, which can be shared and accessed online. Cellulose acetate peels and their digital curation are a valuable and accessible technique for the analysis of coal balls, and physical and digital curation ensures long-term preservation.

Vuruputoor, V. S., D. Monyak, K. C. Fetter, C. Webster, A. Bhattarai, B. Shrestha, S. Zaman, et al. 2023. Welcome to the big leaves: Best practices for improving genome annotation in non-model plant genomes. Applications in Plant Sciences 11(4): e11533.

Figure 4 in the published manuscript contained the following errors. Figure 4A and 4B were missing violin plots for MAKER, which should have been colored green. Figure 4C incorrectly displayed the ideal range of scores—the yellow bar should have spanned 85 to 100 instead of the range shown. Additionally, the color scheme was incorrect. The BRAKER runs should have been colored blue and the StringTie2 runs should have been red. The corrected Figure 4 is presented here with its original caption, which was correct.

We apologize for this error.