Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease ...Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.展开更多
Biopolymers have become popular in geotechnical engineering as they provide a carbon-neutral alternative for soil solidification.Xanthan gum(XG)and jute fiber(JF)were selected to solidify the Yellow River dredged soil...Biopolymers have become popular in geotechnical engineering as they provide a carbon-neutral alternative for soil solidification.Xanthan gum(XG)and jute fiber(JF)were selected to solidify the Yellow River dredged soil.The mechanical behavior of solidified dredged soil(SDS)was investigated using a series of uniaxial compression and splitting tension tests at different XG and JF contents and fiber lengths.The results indicate that on the 28th day,the unconfined compressive strength(UCS)values of SDS samples reached 2.83 MPa and splitting tensile strength(STS)of 0.763 MPa at an XG content of 1.5%.When the JF content was greater than 0.9%,the STS of the SDS samples decreased.This is because that the large fiber content weakened the cementation ability of XG.The addition of JF can significantly increase the strain at peak strength of SDS samples.There is a linear relationship between the UCS and STS of the dredged soils solidified by XG and JF.Microanalysis shows that the strength of SDS samples was improved mainly via the cementation of XG itself and the network structure formed by JF with soil particles.The dredged soil reinforced by XG and JF shows better mechanical performance and has great potential for application.展开更多
基金support from the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,under the Auspices of Project Number:IFP22UQU4281768DSR122.
文摘Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.
文摘以不同自然老化时间的红花种子为材料,采用X射线成像技术检测种子的饱满度,并测定种子发芽率;利用多光谱成像系统采集不同自然老化时间种子的inverse jet图像和不同光谱特征,再用XG-Boost模型进行验证。结果表明,随着自然老化时间的延长,红花种子发芽率显著降低,种子平均反射率与发芽率正相关;筛选出20多个光谱特征与发芽率相关,其中Reflectance Ratio Bands Mean贡献率最高。研究表明,基于光谱成像技术的不同自然老化时间红花种子活力检测研究,筛选出与种子活力相关联参数,实现了红花种子活力的快速无损检测。
基金The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China(Grant Nos.51979267 and 52074143)the Major Science and Technology Program of Inner Mongolia,China(Grant No.2021ZD0007).
文摘Biopolymers have become popular in geotechnical engineering as they provide a carbon-neutral alternative for soil solidification.Xanthan gum(XG)and jute fiber(JF)were selected to solidify the Yellow River dredged soil.The mechanical behavior of solidified dredged soil(SDS)was investigated using a series of uniaxial compression and splitting tension tests at different XG and JF contents and fiber lengths.The results indicate that on the 28th day,the unconfined compressive strength(UCS)values of SDS samples reached 2.83 MPa and splitting tensile strength(STS)of 0.763 MPa at an XG content of 1.5%.When the JF content was greater than 0.9%,the STS of the SDS samples decreased.This is because that the large fiber content weakened the cementation ability of XG.The addition of JF can significantly increase the strain at peak strength of SDS samples.There is a linear relationship between the UCS and STS of the dredged soils solidified by XG and JF.Microanalysis shows that the strength of SDS samples was improved mainly via the cementation of XG itself and the network structure formed by JF with soil particles.The dredged soil reinforced by XG and JF shows better mechanical performance and has great potential for application.
