Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Abstract

The increase in the number of tillers of rice significantly affects grain yield. However, this is measured only by the manual counting of emerging tillers, where the most common method is to count by hand touching. This study develops an efficient, non-destructive method for estimating the number of tillers during the vegetative and reproductive stages under flooded conditions. Unlike popular deep-learning-based approaches requiring training data and computational resources, we propose a simple image-processing pipeline following the empirical principles of synchronously emerging leaves and tillers in rice morphogenesis. Field images were taken by an unmanned aerial vehicle at a very low flying height for UAV imaging—1.5 to 3 m above the rice canopy. Subsequently, the proposed image-processing pipeline was used, which includes binarization, skeletonization, and leaf-tip detection, to count the number of long-growing leaves. The tiller number was estimated from the number of long-growing leaves. The estimated tiller number in a 1.1 m × 1.1 m area is significantly correlated with the actual number of tillers, with 60% of hills having an error of less than ±3 tillers. This study demonstrates the potential of the proposed image-sensing-based tiller-counting method to help agronomists with efficient, non-destructive field phenotyping.

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

“…Fertilizer (15,6,20,11,7 g/m 2 of N, P, K, Mg, and Ca, respectively) was added before sowing. Each plot contained four plants of one accession.…”

“…For instance, remote sensing using unmanned aerial vehicles (UAVs) has become a prominent approach to data collection in the field [4, 5]. Such UAVs are equipped with various sensors, including RGB cameras [6, 7], multispectral cameras [8], and Light Detection and Ranging (LiDAR) devices [9, 10]. Although RGB cameras are commonly employed because of their ease of use, affordability, and minimal maintenance requirements, multispectral cameras are becoming popular for measuring vegetation indices associated with growth and yield [11].…”

An Accurate Classification of Rice Diseases Based on ICAI-V4

“…While the system is capable of obtaining real-time plant images and making predictions, the accuracy of its classification model for plant diseases remains a challenge. Yuki et al proposed a straightforward image processing pipeline that adheres to the empirical principle of the simultaneous emergence of leaves and tillers in rice morphogenesis [14]. The team utilized an unmanned aerial vehicle to capture field images at very low flight altitudes.…”

Large-Scale and High-Accuracy Phenotyping of Populus simonii Leaves Using the Colony Counter and OpenCV

“…It is obvious that image-based phenotyping covers the shortage of these above methods. A few studies have shown that leaf morphological traits can be accurately estimated by the image-based phenotyping [12][13][14].…”