Localizing Small Apples in Complex Apple Orchard Environments
This work addresses automated fruit localization for agricultural applications like yield estimation, but it is incremental as it adapts an existing method to a specific domain.
The paper tackled the problem of localizing small apples in complex orchard environments by adapting the AttentionMask object proposal system, achieving clear performance improvements over standard methods on the MinneApple dataset.
The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples in images of entire apple trees. Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object proposal generation system AttentionMask that focuses on small objects. We adapt AttentionMask by either adding a new module for very small apples or integrating it into a tiling framework. Both approaches clearly outperform standard object proposal generation systems on the MinneApple dataset covering complex apple orchard environments. Our evaluation further analyses the improvement w.r.t. the apple sizes and shows the different characteristics of our two approaches.