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您的检索式：作者名="Anna O.Conrad"
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| 1 | Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles显示文摘Early detection of plant diseases,prior to symptom development,can allow for targeted and more proactive disease management.The objective of this study was to evaluate the use of near-infrared(NIR)spectroscopy combined with machine learning for early detection of rice sheath blight(ShB),caused by the fungus Rhizoctonia solani.We collected NIR spectra from leaves of ShBsusceptible rice(Oryza sativa L.)cultivar,Lemont,growing in a growth chamber one day following inoculation with R.solani,and prior to the development of any disease symptoms.Support vector machine(SVM)and random forest,two machine learning algorithms,were used to build and evaluate the accuracy of supervised classification-based disease predictive models.Sparse partial least squares discriminant analysis was used to confirm the results.The most accurate model comparing mockinoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1%(N=72),while when control,mock-inoculated,and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3%(N=105).These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development.While testing and validation in field trials are still needed,this technique holds promise for application in the field for disease diagnosis and management. | Anna O.Conrad Wei Li Da-Young Lee Guo-Liang Wang Luis Rodriguez-Saona Pierluigi Bonello | 2020 | Plant Phenomics2020，2，1： | 3 |
| 2 | Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening显示文摘Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees. | Xing Wei Jinnuo Zhang Anna O.Conrad Charles E.Flower Cornelia C.Pinchot Nancy Hayes-Plazolles Ziling Chen Zhihang Song Songlin Fei Jian Jin | 2023 | Artificial Intelligence in Agriculture2023，，4： | 0 |
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