Abstract
This work introduces a method that combines remote sensing and deep learning into a framework that is tailored for accurate, reliable and efficient counting and sizing of plants in aerial images. The investigated task focuses on two low-density crops, potato and lettuce. This double objective of counting and sizing is achieved through the detection and segmentation of individual plants by fine-tuning an existing deep learning architecture called Mask R-CNN. This paper includes a thorough discussion on the optimal parametrisation to adapt the Mask R-CNN architecture to this novel task. As we examine the correlation of the Mask R-CNN performance to the annotation volume and granularity (coarse or refined) of remotely sensed images of plants, we conclude that transfer learning can be effectively used to reduce the required amount of labelled data. Indeed, a previously trained Mask R-CNN on a low-density crop can improve performances after training on new crops. Once trained for a given crop, the Mask R-CNN solution is shown to outperform a manually-tuned computer vision algorithm. Model performances are assessed using intuitive metrics such as Mean Average Precision (mAP) from Intersection over Union (IoU) of the masks for individual plant segmentation and Multiple Object Tracking Accuracy (MOTA) for detection. The presented model reaches an mAP of 0.418 for potato plants and 0.660 for lettuces for the individual plant segmentation task. In detection, we obtain a MOTA of 0.781 for potato plants and 0.918 for lettuces.
Highlights
Despite the widely accepted importance of agriculture as one of the main human endeavours related to sustainability, environment and food supply, it is only recently that many data science use cases to agricultural lands have been unlocked by engineering innovations [1,2]
Models yielded poor results when trained on a first specific dataset and used in inference on a second one with other types of objects. This justifies the necessity of understanding the complex parameterization process of Mask R-Convolutional Neural Network (CNN) and this study is the first one, to the best of our knowledge, which disseminates in detail the nodes and effects of this complex model
Combining remote sensing and deep learning for plant counting and sizing using the Mask R-CNN architecture embodies a direct and automatic cutting-edge approach. It outperforms the manually-parametrized computer vision baseline requiring multiple processing steps when used for plant detection
Summary
Despite the widely accepted importance of agriculture as one of the main human endeavours related to sustainability, environment and food supply, it is only recently that many data science use cases to agricultural lands have been unlocked by engineering innovations (e.g., variable rate sprayers) [1,2]. Two research domains are heavily contributing to this agriculture paradigm shift: remote sensing and artificial intelligence. Artificial intelligence induces informed management decisions by extracting appropriate farm analytics in a fine-grained scale. In the research frontline of the remote sensing/artificial intelligence intersection lies the accurate, reliable and computationally efficient extraction of plant-level analytics, i.e., analytics that are estimated for each and every individual plant of a field [3]. By identifying each and every potato, farm managers could estimate the emergence rate (the percentage of seeded potatoes that emerged), target the watering strategy to the crop and predict the yield, while the counting and sizing of lettuces determine the harvest and optimise the logistics
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