Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.
Gray leaf spot, common rust, and northern leaf blight are three common maize leaf diseases that cause great economic losses to the worldwide maize industry. Timely and accurate disease identification can reduce economic losses, pesticide usage, and ensure maize yield and food security. Deep learning methods, represented by convolutional neural networks (CNNs), provide accurate, effective, and automatic diagnosis on server platforms when enormous training data is available. Restricted by dataset scale and application scenarios, CNNs are difficult to identify small-scale data sets on mobile terminals, while the lightweight networks, designed for the mobile terminal, achieve a better balance between efficiency and accuracy. This paper proposes a two-staged deep-transfer learning method to identify maize leaf diseases in the field. During the deep learning period, 8 deep and 4 lightweight CNN models were trained and compared on the Plant Village dataset, and ResNet and MobileNet achieved test accuracy of 99.48% and 98.69% respectively, which were then migrated onto the field maize leave disease dataset collected on mobile phones. By using layer-freezing and fine-tuning strategies on ResNet and MobileNet, fine-tuned MobileNet achieved the best accuracy of 99.11%. Results confirmed that disease identification performance from lightweight CNNs was not inferior to that of deep CNNs and transfer learning training efficiency was higher when lacking training samples. Besides, the smaller gaps between source and target domains, the better the identification performance for transfer learning. This study provides an application example for maize disease identification in the field using deep-transfer learning and provides a theoretical basis for intelligent maize leaf disease identification from images captured with mobile devices.
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