14 research outputs found
Towards Robust Plant Disease Diagnosis with Hard-sample Re-mining Strategy
With rich annotation information, object detection-based automated plant
disease diagnosis systems (e.g., YOLO-based systems) often provide advantages
over classification-based systems (e.g., EfficientNet-based), such as the
ability to detect disease locations and superior classification performance.
One drawback of these detection systems is dealing with unannotated healthy
data with no real symptoms present. In practice, healthy plant data appear to
be very similar to many disease data. Thus, those models often produce
mis-detected boxes on healthy images. In addition, labeling new data for
detection models is typically time-consuming. Hard-sample mining (HSM) is a
common technique for re-training a model by using the mis-detected boxes as new
training samples. However, blindly selecting an arbitrary amount of hard-sample
for re-training will result in the degradation of diagnostic performance for
other diseases due to the high similarity between disease and healthy data. In
this paper, we propose a simple but effective training strategy called
hard-sample re-mining (HSReM), which is designed to enhance the diagnostic
performance of healthy data and simultaneously improve the performance of
disease data by strategically selecting hard-sample training images at an
appropriate level. Experiments based on two practical in-field eight-class
cucumber and ten-class tomato datasets (42.7K and 35.6K images) show that our
HSReM training strategy leads to a substantial improvement in the overall
diagnostic performance on large-scale unseen data. Specifically, the object
detection model trained using the HSReM strategy not only achieved superior
results as compared to the classification-based state-of-the-art
EfficientNetV2-Large model and the original object detection model, but also
outperformed the model using the HSM strategy
Uncovering of major genetic factors generating naturally occurring variation in heading date among Asian rice cultivars
To dissect the genetic factors controlling naturally occurring variation of heading date in Asian rice cultivars, we performed QTL analyses using F2 populations derived from crosses between a japonica cultivar, Koshihikari, and each of 12 cultivars originating from various regions in Asia. These 12 diverse cultivars varied in heading date under natural field conditions in Tsukuba, Japan. Transgressive segregation was observed in 10 F2 combinations. QTL analyses using multiple crosses revealed a comprehensive series of loci involved in natural variation in flowering time. One to four QTLs were detected in each cross combination, and some QTLs were shared among combinations. The chromosomal locations of these QTLs corresponded well with those detected in other studies. The allelic effects of the QTLs varied among the cross combinations. Sequence analysis of several previously cloned genes controlling heading date, including Hd1, Hd3a, Hd6, RFT1, and Ghd7, identified several functional polymorphisms, indicating that allelic variation at these loci probably contributes to variation in heading date. Taken together, the QTL and sequencing results indicate that a large portion of the phenotypic variation in heading date in Asian rice cultivars could be generated by combinations of different alleles (possibly both loss- and gain-of-function) of the QTLs detected in this study
Use of crude sap for one-step RT-PCR-based assays of Bean yellow mosaic virus and the utility of this protocol for various plant?virus combinations
Selection of an attenuated isolate of Bean yellow mosaic virus for protection of dwarf gentian plants from viral infection in the field
Biotype identification of Bemisia tabaci by acoustical method
Bemisia tabaci has two major biotypes: B and Q. Biotype identification is necessary for whitefly control, since different biotypes have different pesticide resistance. However, slow and expensive techniques are needed for accurate biotype classification. In this paper, we propose a whitefly biotype identification scheme using an acoustic signature, and evaluate its performance. The proposed scheme achieves biotype identification by three steps: signal detection, frequency-domain matching, and classification of biotypes. We evaluated the performance of the proposed scheme by processing actual whitefly sounds obtained in a recording experiment, and calculated the accuracy of the classification.Results showed the proposed biotype identification method achieved a correct detection rate of 92% in Bemisia tabaci. This result suggests that the proposed scheme is a viable alternative for biotype identification of whitefly