Large-Scale Counting and Localization of Pineapple Inflorescence Through Deep Density-Estimation.

Abstract

Natural flowering affects fruit development and quality, and impacts the harvest of specialty plants like pineapple. Pineapple growers use chemicals to induce flowering so that most plants within a field produce fruit of high quality that is ready to harvest at the same time. Since pineapple is hand-harvested, the ability to harvest all of the fruit of a field in a single pass is critical to reduce field losses, costs, and waste, and to maximize efficiency. Traditionally, due to high planting densities, pineapple growers have been limited to gathering crop intelligence through manual inspection around the edges of the field, giving them only a limited view of their crop's status. Through the advances in remote sensing and computer vision, we can enable the regular inspection of the field and automated inflorescence counting enabling growers to optimize their management practices. Our work uses a deep learning-based density estimation approach to count the number of flowering pineapple plants in a field with a test MAE of 11.5 and MAPD of 6.37%. Notably, the computational complexity of this method does not depend on the number of plants present and therefore efficiently scale to easily detect over a 1.6 million flowering plants in a field. We further embed this approach in an active learning framework for continual learning and model improvement.

Plant phenotyping relevance

パイナップルの開花状態を画像から自動計数・位置推定する深層学習手法が研究の中心であり、精度評価と継続学習も行っているため。

Code and data availability

The paper's pineapple inflorescence imagery and point-annotation datasets are not publicly available; access requires contacting the corresponding author. No public code, model checkpoints, or dataset URLs are provided (the only URL, NVIDIA TensorRT, is a generic library footnote).

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