Agricultural Mobile Robots for Plant Health Assessment and Drought Stress Detection

Abstract

The vulnerability of plants to various threats, such as insects, pathogens, and weeds, poses a significant risk to food security, particularly before harvest. Mobile robots are used in digital agriculture as a breakthrough approach to address challenges in crop production, such as plant health assessment and drought stress detection. This chapter aims to explore the application of agricultural mobile robots equipped with advanced sensing technologies and computer vision algorithms, along with their key features, to enhance crop management practices. An overview of some the platforms with different steering mechanisms, sensors, interfaces, communication, and machine learning has been provided along with case studies on the use of robots for collecting data on plant health indicators such as physiological parameters, leaf coloration, and soil moisture levels. Recent trends in this area show that by utilizing machine learning techniques such as convolutional neural networks (CNNs) and support vector machines (SVMs), the collected data are analyzed to identify symptoms of plant diseases, nutrient deficiencies, and drought stress, facilitating timely interventions to mitigate crop losses. The integration of Internet of robotic things into existing practices are also discussed with respect to cost-effectiveness, scalability, and user acceptance.