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.
Highlights
light detection and ranging (LIDAR) absorption spectroscopy systems have been proposed as a feasible method to remotely sense the atmospheric components as the laser beam at the detector can discern the characteristics of the medium the beam passed through [105]
This ongoing evolution is increasingly driven by the application of artificial intelligence (AI) to perperform nonstandard tasks, which expand the range of activities that can be performed form nonstandard tasks, which expand the range of activities that can be performed in in precision agriculture to allow for data-driven decision-making from the operators
This review examined current and likely future electro-optical remote sensing applications for precision agriculture, with a focus on novel methods for early detection of plant diseases and the increasing adoption of spectral analysis in food quality assessment
Summary
Apart from the cost to production, the use of pesticides comes with an increased risk of toxic residue on crops and to the surrounding environ protective measure can be excessive and expensive. To in the use of pesticides comes with an increased risk of toxic residue on crops and to the crease production cost efficiency and yield, management need the information to be able surrounding environment and ecosystems, as well as health implications for agriculture to identify disease andcost pestefficiency epidemics and at theneed earliest workers [4].and. To quantify increase production andaccurately yield, management the possible stage This allows for contained, localised treatment, reducing the use of pesticides and information to be able to identify and quantify disease and pest epidemics accurately and potential yield losses.
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