Hybrid Convolutional Neural Network for Plant Diseases Prediction

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.

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

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).