The paper presents design of a robotic vehicle with real-time video streaming. Prior to advancements in technology, military personnel and wild life researchers were used for these tasks (Intelligence gathering, Surveillance and Reconnaissance), and would risk injury and possible death to accomplish them. The need for safer means of performing these surveillance operations have become a pressing need over the years. Hence, this work aimed at developing a Robotic Vehicle capable of providing real-time video surveillance for military and wild life research operation applications. The Robotic Vehicle is made up of radio frequency based remote control, a PWM-enabled motor driver IC for efficient mobility, an OV2460 camera, and ESP32-CAM with Wi-Fi enable for video streaming without internet connection. The ESP32-CAM also serves as a web server which can be accessed by any device's browser connected via Hotspot setup. The robotic vehicle tested successfully and communicates effectively with remote control unit on the average of 0.5km distance.
This paper presents Predictive Model for Stem Borers’ classification in Precision Farming. The recent announcement of the aggressive attack of stem borers (Spodoptera species) to maize crops in Africa is alarming. These species migrate in large numbers and feed on maize leaf, stem, and ear of corn. The male of these species are the target because after mating with their female counterpart, thousands of eggs are laid which produces larvae that create the havoc. Currently, Nigerian farmers find it difficult to distinguish between these targeted species (Fall Armyworm-FAW, African Armyworm-AAW and Egyptian cotton leaf worm-ECLW only) because they look alike in appearance. For these reasons, the network model that would predict the presence of these species in the maize farm to farmers is proposed. The maize species were captured using delta pheromone traps and laboratory breeding for each category. The captured images were pre-processed and stored in an online Google drive image dataset folder created. The convolutional neural network (CNN) model for classifying these targeted maize moths was designed from the scratch. The Google Colab platform with Python libraries was used to train the model called MothNet. The images of the FAW, AAW, and ECLW were inputted to the designed MothNet model during learning process. Dropout and data augmentation were added to the architecture of the model for an efficient prediction. After training the MothNet model, the validation accuracy achieved was 90.37% with validation loss of 24.72%, and training accuracy 90.8% with loss of 23.25%, and the training occurred within 5minutes 33seconds. Due to the small amount of images gathered (1674), the model prediction on each image was of low confident. Because of this, transfer learning was deployed and Resnet 50 pretrained model selected and modified. The modified ResNet-50 model was fine-tuned and tested. The model validation accuracy achieved was 99.21%, loss of 3.79%, and training accuracy of 99.75% with loss of 2.55% within 10mins 5 seconds. Hence, MothNet model can be improved on by gathering more images and retraining the system for optimum performance while modified ResNet 50 is recommended to be integrated in Internet of Things device for maize moths’ classification on-site.
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