In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
Putrescine(Put)as the compound of plant polyamines is catalyzed by arginine decarboxylase(ADC),which is encoded by two members,ADC1 and ADC2 in Arabidopsis,and ADC2 is mainly responsible for Put biosynthesis.Accumulat...Putrescine(Put)as the compound of plant polyamines is catalyzed by arginine decarboxylase(ADC),which is encoded by two members,ADC1 and ADC2 in Arabidopsis,and ADC2 is mainly responsible for Put biosynthesis.Accumulated evidence demonstrates the important function of Put in plant growth and development,but its role in regulating seed germination under high temperature(HT)has not been reported yet.SOMNUS(SOM)is the negative regulator for seed germination thermoinhibition by altering downstream gibberellin(GA)and abscisic acid(ABA)metabolism.In this study,we found exogenous application of Put obviously alleviated the inhibition effect of HT on seed germination.Whereas pharmacological inhibition of endogenous Put level reduced seed germination under HT.Consistently,HT induced the rapid accumulation of Put level,and the adc2 mutant defi-ciency in Put biosynthesis also showed more sensitivity to HT stress.Furthermore,we found that the Put signal suppressed the expression of SOM and changed the transcriptional patterns of genes associated with GA/ABA metabolism.Genetic analysis also revealed SOM was epistatic to ADC2 to alter GA/ABA metabolism.Collectively,our finding reveals the novel function of Put in controlling seed germination under HT through SOM,and provides the possibility to develop Put as the innovational regulator for uniform seed germination under HT stress.展开更多
为了降低传统增量型Σ-ΔADC在同精度情况下的量化时钟周期数,提高转换速率,提出了1种采用粗细量化的2步式增量放大型ADC.该ADC采用SAR ADC先进行6位粗量化,再采用增量型Σ-ΔADC进行8位高精度位的细量化,通过数字码拼接完成最终量化结...为了降低传统增量型Σ-ΔADC在同精度情况下的量化时钟周期数,提高转换速率,提出了1种采用粗细量化的2步式增量放大型ADC.该ADC采用SAR ADC先进行6位粗量化,再采用增量型Σ-ΔADC进行8位高精度位的细量化,通过数字码拼接完成最终量化结果.同时引入了1种增益自举C类反相器技术,有效地降低了供电电压和整体功耗.该ADC使用0.18μm标准CMOS工艺进行了电路实现,在1.2 V供电电压,1 MHz采样频率、10 k S/s的转换速率的情况下,达到了81.26 d B的信噪失真比(SNDR)和13.21位的有效位数(ENOB),最大积分非线性为0.8 LSB.并且该ADC的整体功耗为197μW,可用于低电压低功耗的仪器测量和传感器等领域.展开更多
The effects of Mg enhancement and heat treatment on the microstructures and tensile properties of Al_2Ca-added ADC12 die casting alloys were investigated. 0.3% and 0.5% Mg in the form of a master alloy including a tra...The effects of Mg enhancement and heat treatment on the microstructures and tensile properties of Al_2Ca-added ADC12 die casting alloys were investigated. 0.3% and 0.5% Mg in the form of a master alloy including a trace amount of Al_2Ca were added to conventional ADC12（383 and AlSi10Cu2Fe） alloy with an initial Mg-content of 0.3% to increase the Mg content to 0.6% and 0.8%, respectively. To avoid heat treatmentinduced surface blisters, shortened solution treatment for 15 min at 490 ℃ and artificial aging for 6 h at 150 ℃ was undertaken. The results show that a 10% improvement in the shape factor of eutectic Si particles was achieved for Al_2Ca-added ADC12 with 0.8% Mg compared to the conventional ADC12 in the as-aged condition. Al_2Ca-added ADC12 with 0.8% Mg exhibited a yield strength of 289 MPa, a tensile strength of 407 MPa, and an elongation of 4.22%.展开更多
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
基金the National Natural Science Foundation of China(Grant No.32170562).
文摘Putrescine(Put)as the compound of plant polyamines is catalyzed by arginine decarboxylase(ADC),which is encoded by two members,ADC1 and ADC2 in Arabidopsis,and ADC2 is mainly responsible for Put biosynthesis.Accumulated evidence demonstrates the important function of Put in plant growth and development,but its role in regulating seed germination under high temperature(HT)has not been reported yet.SOMNUS(SOM)is the negative regulator for seed germination thermoinhibition by altering downstream gibberellin(GA)and abscisic acid(ABA)metabolism.In this study,we found exogenous application of Put obviously alleviated the inhibition effect of HT on seed germination.Whereas pharmacological inhibition of endogenous Put level reduced seed germination under HT.Consistently,HT induced the rapid accumulation of Put level,and the adc2 mutant defi-ciency in Put biosynthesis also showed more sensitivity to HT stress.Furthermore,we found that the Put signal suppressed the expression of SOM and changed the transcriptional patterns of genes associated with GA/ABA metabolism.Genetic analysis also revealed SOM was epistatic to ADC2 to alter GA/ABA metabolism.Collectively,our finding reveals the novel function of Put in controlling seed germination under HT through SOM,and provides the possibility to develop Put as the innovational regulator for uniform seed germination under HT stress.
文摘为了降低传统增量型Σ-ΔADC在同精度情况下的量化时钟周期数,提高转换速率,提出了1种采用粗细量化的2步式增量放大型ADC.该ADC采用SAR ADC先进行6位粗量化,再采用增量型Σ-ΔADC进行8位高精度位的细量化,通过数字码拼接完成最终量化结果.同时引入了1种增益自举C类反相器技术,有效地降低了供电电压和整体功耗.该ADC使用0.18μm标准CMOS工艺进行了电路实现,在1.2 V供电电压,1 MHz采样频率、10 k S/s的转换速率的情况下,达到了81.26 d B的信噪失真比(SNDR)和13.21位的有效位数(ENOB),最大积分非线性为0.8 LSB.并且该ADC的整体功耗为197μW,可用于低电压低功耗的仪器测量和传感器等领域.
文摘The effects of Mg enhancement and heat treatment on the microstructures and tensile properties of Al_2Ca-added ADC12 die casting alloys were investigated. 0.3% and 0.5% Mg in the form of a master alloy including a trace amount of Al_2Ca were added to conventional ADC12（383 and AlSi10Cu2Fe） alloy with an initial Mg-content of 0.3% to increase the Mg content to 0.6% and 0.8%, respectively. To avoid heat treatmentinduced surface blisters, shortened solution treatment for 15 min at 490 ℃ and artificial aging for 6 h at 150 ℃ was undertaken. The results show that a 10% improvement in the shape factor of eutectic Si particles was achieved for Al_2Ca-added ADC12 with 0.8% Mg compared to the conventional ADC12 in the as-aged condition. Al_2Ca-added ADC12 with 0.8% Mg exhibited a yield strength of 289 MPa, a tensile strength of 407 MPa, and an elongation of 4.22%.
