On the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
Raiyan Rahman Christopher Indris Tianxiao Zhang Kaidong Li Brian McCornack Daniel Flippo Ajay Sharda Guanghui Wang
Aphid infestations can cause extensive damage to wheat and sorghum fields and
spread plant viruses, resulting in significant yield losses in agriculture. To
address this issue, farmers often rely on chemical pesticides, which are
inefficiently applied over large areas of fields. As a result, a considerable
amount of pesticide is wasted on areas without pests, while inadequate amounts
are applied to areas with severe infestations. The paper focuses on the urgent
need for an intelligent autonomous system that can locate and spray
infestations within complex crop canopies, reducing pesticide use and
environmental impact. We have collected and labeled a large aphid image dataset
in the field, and propose the use of real-time semantic segmentation models to
segment clusters of aphids. A multiscale dataset is generated to allow for
learning the clusters at different scales. We compare the segmentation speeds
and accuracy of four state-of-the-art real-time semantic segmentation models on
the aphid cluster dataset, benchmarking them against nonreal-time models. The
study results show the effectiveness of a real-time solution, which can reduce
inefficient pesticide use and increase crop yields, paving the way towards an
autonomous pest detection system.