Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Abstract

In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

Aplicação da nanotecnologia na engenharia de alimentos: uma revisão

“…Visando o acompanhamento e a supervisão de diferentes processos, a utilização de sensores apresenta-se como uma alternativa não destrutiva voltada para a quantificação de agentes patógenos em alimentos. Com o aprimoramento das técnicas e tecnologias desse ramo, é possível promover uma produção de maior precisão, voltada para o monitoramento da ação biótica e abiótica, resultando em maior aproveitamento e qualidade dos produtos (Fahey et al, 2021).…”

Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications

“…Airborne remote sensing, in particular, allows for detailed analysis of vast areas, with flexibility of data capture, and high accuracy at various grazing angles across different terrains. These features are particularly important in the application to forest monitoring and food crop health-status assessment [11,12]. In geophysics applications, in combination with GNSS, highly accurate data in measuring faults and changes can be detected.…”

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

“…However, they are sensitive to weather conditions, are time consuming, and provide low temporal resolution. Thermal infrared (TIR) collects the variation in leaf and canopy dy-namics for leaf temperature estimation using thermal imagery [30]. It is a noncontact, less laborious, and nondestructive method; however, the system shows inaccuracies in yield estimation, crop water stress, and plant growth determination [31][32][33][34].…”