A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards
This work addresses yield mapping in apple orchards, providing incremental improvements by combining existing methods for more accurate fruit counting.
The study tackled apple detection and counting for yield mapping by comparing classical and deep learning methods, finding that classical detection outperformed deep learning in most datasets, but deep learning counting was better, achieving yield accuracies of 95.56% to 97.83% when combined.
We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.