DataSheet_1_Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.zip

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning–based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.</p

Additional file 3: Table S3. of Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

Summary of genes common for both XP-EHH and XP-CLR test statistics. (XLS 45 kb

Additional file 4: Table S4. of Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

Gene Ontology Biological Process terms obtained from DAVID gene ontology analysis using all XP-EHH and XP-CLR gene lists. (XLS 47 kb

Additional file 5: Figure S1. of Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

Tajima’s D and FST plot of positively selected gene regions in Sanga and indicus cattle populations. The Tajima’s D plot for each gene region (upper plot for each gene) is the Tajima’s D value within 50 kb window plotted for both populations. The smaller (negative) Tajima’s D value in Sanga population shows that the gene region considered is under positive selection. The FST plot (lower plot for each gene) is the FST values within 50 kb windows separated by 5 kb steps. (DOCX 300 kb

Additional file 6: Table S5. of Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

Names and descriptions of major candidate meat quality related genes. All gene names and descriptions are based on RefSeq. (DOCX 24 kb