IoT-based Plant Health Analysis using Optical Sensors in Precision Agriculture

Abstract

To support the current population growth, modern agriculture must increase food production while reducing the use of water and other resources required for crop cultivation. Precision agriculture (PA) aims to achieve these via a variety of methods that include site-specific plant selection, variable rate irrigation and fertilisation, as well as site-specific pesticide and herbicide application. To determine the plant performance and health that drive such precision PA practices, PA solutions currently collect and analyse data from cameras and multispectral sensors. Technological advancements in the Internet of Things (IoT) and in the development of Unmanned Aerial Vehicles (UAV) in recent years have provided potential solutions for automating image acquisition and analysis that can advance such PA practices. This paper proposes 1) devising plant models from RGB and multi-spectral data, 2) using such models to guide the above PA practices. More specifically, the paper explores monitoring plants at different health and life cycle stages from fully green to completely dry and capturing related RGB and multi-spectral data in a controlled environment. These data are then analysed to create a model for each plant variety, which we refer to as the plant profile, that captures the combined colour and light reflectance of the plant over its life cycle and related health stages. The paper proposes using such plant variety profiles to determine the performance and health of the plants across entire crops. Finally, the paper discusses how UAVs and IoT can be used to automatically capture and analyse the images and multi-spectral data for advancing PA.