Towards phenological development quantification in Brassica plants via multispectral imaging and deep learning

Abigail A. Stone; Srijith Rajeev; Shishir P. Rao; Karen A. Panetta; Sos S. Agaian

doi:10.1117/12.3014027

7 June 2024 Towards phenological development quantification in Brassica plants via multispectral imaging and deep learning

Abigail A. Stone, Srijith Rajeev, Shishir P. Rao, Karen A. Panetta, Sos S. Agaian

Author Affiliations +

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330A (2024) https://doi.org/10.1117/12.3014027
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

As global populations soar and the climate warms, food supply management is an increasingly critical problem. Precision agriculture, driven by on-site data collected from various sensors, plays a pivotal role in optimizing irrigation, fertilization, and enhancing plant health and crop yield. However, the manual process of in-filed chlorophyll measurement, which is a key metric for guiding agricultural decisions, is very cumbersome and poses significant challenges. This paper explores the transformative potential of multispectral imaging data to automate plant measuring and monitoring tasks, thereby reducing labor and time costs while improving the quality of data available for making informed agricultural decisions. We present a deep-learning model for instance segmentation of plants, trained on the Growliflower dataset of RGB and multispectral image cubes of cauliflower plants. The proposed algorithm uses a Convolutional Neural Network (CNN) to leverage both the spectral information and and its spatial context to locate individual plants. We introduce a novel band-selection algorithm for determining the most significant multispectral features for use in the convolutional network: this reduces model complexity while ensuring accurate results. Our model’s ability to generalize across varying growth stages, soil conditions, and varieties of crops in the training dataset demonstrates its suitability for real-world agricultural applications. This fusion of cutting-edge sensing technology for robotic systems and state-of-the-art deep learning models holds significant promise for advancements in crop yield, resource efficiency, and sustainability practices.

Conference Presentation

Citation Download Citation

Abigail A. Stone, Srijith Rajeev, Shishir P. Rao, Karen A. Panetta, and Sos S. Agaian "Towards phenological development quantification in Brassica plants via multispectral imaging and deep learning", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330A (7 June 2024); https://doi.org/10.1117/12.3014027

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