Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products.The monitoring of plant health continuously and detecting the diseases is a significant f...Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products.The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agri-culture.Manual system to monitor the diseases in plant is time consuming and report a lot of errors.There is high demand for technology to detect the plant dis-eases automatically.Recently image processing approach and deep learning approach are highly invited in detection of plant diseases.The diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in plants.These are the main reasons to affects the plants life and yield.To identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the diseases.First the leaf images of plants are collected and preprocessed to remove noisy data in image.Further data normalization,augmentation and removal of background noises are done.The images are divided as testing and training,training images are fed as input to deep learning architecture.First,we identify the region of interest(RoI)by using selective search.In every region,feature of convolutional neural network(CNN)is extracted independently for further classification.The plants such as tomato,potato and bell pepper are taken for this experiment.The plant input image is analyzed and classify as healthy plant or unhealthy plant.If the image is detected as unhealthy,then type of diseases the plant is affected will be displayed.Our proposed technique achieves 98.5%of accuracy in predicting the plant diseases.展开更多
Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic instal...Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.展开更多
As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,...As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).展开更多
基金Supporting Project Number(RSP-2021/323),King Saud University,Riyadh,Saudi Arabia。
文摘Plant diseases prediction is the essential technique to prevent the yield loss and gain high production of agricultural products.The monitoring of plant health continuously and detecting the diseases is a significant for sustainable agri-culture.Manual system to monitor the diseases in plant is time consuming and report a lot of errors.There is high demand for technology to detect the plant dis-eases automatically.Recently image processing approach and deep learning approach are highly invited in detection of plant diseases.The diseases like late blight,bacterial spots,spots on Septoria leaf and yellow leaf curved are widely found in plants.These are the main reasons to affects the plants life and yield.To identify the diseases earliest,our research presents the hybrid method by com-bining the region based convolutional neural network(RCNN)and region based fully convolutional networks(RFCN)for classifying the diseases.First the leaf images of plants are collected and preprocessed to remove noisy data in image.Further data normalization,augmentation and removal of background noises are done.The images are divided as testing and training,training images are fed as input to deep learning architecture.First,we identify the region of interest(RoI)by using selective search.In every region,feature of convolutional neural network(CNN)is extracted independently for further classification.The plants such as tomato,potato and bell pepper are taken for this experiment.The plant input image is analyzed and classify as healthy plant or unhealthy plant.If the image is detected as unhealthy,then type of diseases the plant is affected will be displayed.Our proposed technique achieves 98.5%of accuracy in predicting the plant diseases.
基金funded by the Spanish Ministerio de Ciencia,Innovación y Universidades,grant number RTC2019-007364-3(FPGM)by the Comunidad de Madrid through the direct grant with ref.SI4/PJI/2024-00233 for the promotion of research and technology transfer at the Universidad Autónoma de Madrid。
文摘Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.
基金supported by the Shandong Provincial Natural Science Foundation,China(No.ZR2021YQ43)the National Natural Science Foundation of China(Nos.U1933135 and 61931021)the Major Science and Technology Project of Shandong Province,China(No.2019JZZY010415)。
文摘As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).
