Seed Classification using Synthetic Image Datasets Generated from Low-Altitude UAV Imagery

Plant breeding programs extensively monitor the evolution of seed kernels for seed certification, wherein lies the need to appropriately label the seed kernels by type and quality. However, the breeding environments are large where the monitoring of seed kernels can be challenging due to the minuscule size of seed kernels. The use of unmanned aerial vehicles aids in seed monitoring and labeling since they can capture images at low altitudes whilst being able to access even the remotest areas in the environment. A key bottleneck in the labeling of seeds using UAV imagery is drone altitude i.e. the classification accuracy decreases as the altitude increases due to lower image detail. Convolutional neural networks are a great tool for multi-class image classification when there is a training dataset that closely represents the different scenarios that the network might encounter during evaluation. The article addresses the challenge of training data creation using Domain Randomization wherein synthetic image datasets are generated from a meager sample of seeds captured by the bottom camera of an autonomously driven Parrot AR Drone 2.0. Besides, the article proposes a seed classification framework as a proof-of-concept using the convolutional neural networks of Microsoft's ResNet-100, Oxford's VGG-16, and VGG-19. To enhance the classification accuracy of the framework, an ensemble model is developed resulting in an overall accuracy of 94.6%.

PiBase: An IoT-based Security System using Raspberry Pi and Google Firebase

Smart environments are environments where digital devices are connected to each other over the Internet and operate in sync. Security is of paramount importance in such environments. This paper addresses aspects of authorized access and intruder detection for smart environments. Proposed is PiBase, an Internet of Things (IoT)-based app that aids in detecting intruders and providing security. The hardware for the application consists of a Raspberry Pi, a PIR motion sensor to detect motion from infrared radiation in the environment, an Android mobile phone and a camera. The software for the application is written in Java, Python and NodeJS. The PIR sensor and Pi camera module connected to the Raspberry Pi aid in detecting human intrusion. Machine learning algorithms, namely Haar-feature based cascade classifiers and Linear Binary Pattern Histograms (LBPH), are used for face detection and face recognition, respectively. The app lets the user create a list of non-intruders and anyone that is not on the list is identified as an intruder. The app alerts the user only in the event of an intrusion by using the Google Firebase Cloud Messaging service to trigger a notification to the app. The user may choose to add the detected intruder to the list of non-intruders through the app to avoid further detections as intruder. Face detection by the Haar Cascade algorithm yields a recall of 94.6%. Thus, the system is both highly effective and relatively low cost.