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

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