Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Zixuan Teng; Jiawei Chen; Jian Wang; Shuixiu Wu; Riqing Chen; Yaohai Lin; Liyan Shen; Robert Jackson; Ji Zhou; Changcai Yang

doi:10.34133/plantphenomics.0105

Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Plant Phenomics. 2023 Oct 16:5:0105. doi: 10.34133/plantphenomics.0105. eCollection 2023.

Authors

Zixuan Teng^{1

2}, Jiawei Chen³, Jian Wang⁴, Shuixiu Wu^{1

5}, Riqing Chen¹, Yaohai Lin¹, Liyan Shen³, Robert Jackson⁶, Ji Zhou^{3

6}, Changcai Yang^{1

7}

Affiliations

¹ Digital Fujian Research Institute of Big Data for Agriculture and Forestry, College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
² Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University), Fujian Province University, Fuzhou 350002, China.
³ State Key Laboratory of Crop Genetics & Germplasm Enhancement, academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China.
⁴ Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan 750002, China.
⁵ College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
⁶ Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK.
⁷ Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.

Abstract

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties' yield performance, key yield-related traits such as panicle number per unit area (PNpM²) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM² trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM² trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM² trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM² trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.