Smart Sensor for Real-Time Quantification of Common Symptoms Present in Unhealthy Plants

Abstract

Plant responses to physiological function disorders are called symptoms and they are caused principally by pathogens and nutritional deficiencies. Plant symptoms are commonly used as indicators of the health and nutrition status of plants. Nowadays, the most popular method to quantify plant symptoms is based on visual estimations, consisting on evaluations that raters give based on their observation of plant symptoms; however, this method is inaccurate and imprecise because of its obvious subjectivity. Computational Vision has been employed in plant symptom quantification because of its accuracy and precision. Nevertheless, the systems developed so far lack in-situ, real-time and multi-symptom analysis. There exist methods to obtain information about the health and nutritional status of plants based on reflectance and chlorophyll fluorescence, but they use expensive equipment and are frequently destructive. Therefore, systems able of quantifying plant symptoms overcoming the aforementioned disadvantages that can serve as indicators of health and nutrition in plants are desirable. This paper reports an FPGA-based smart sensor able to perform non-destructive, real-time and in-situ analysis of leaf images to quantify multiple symptoms presented by diseased and malnourished plants; this system can serve as indicator of the health and nutrition in plants. The effectiveness of the proposed smart-sensor was successfully tested by analyzing diseased and malnourished plants.

Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry

“…Similarly, electronic-nose devices have been used to determine the presence of damaging insects in wood (e.g., termites) [61], to identify tree diseases [106], and detect other microbial pests that have significant impacts on the present status of forest-stand health and future tree merchantability following tree harvests. Visual assessments to confirm plant-health status, determined with e-nose instruments, also are possible via image analysis of plant symptoms using smart optical sensors [322]. …”

Modelling approaches for addressing complexity in plant health management

“…The area under the Disease Progress Curve calculated from these models is considered to represent a direct measure of the resistance of a genotype (Haynes and Weingartner, 2003;Contreras et al, 2009).…”

A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing

“…Thresholding, also referred to as binarization, consists of defining one or more threshold value(s) that define the range of pixel values that are associated with the objects of interest, such that only these remain after the thresholding procedure. There are many challenges in determining thresholds, which explains why this value is often defined manually (Bock et al 2008;Cui et al 2010;Kwack et al 2005;Contreras-Medina et al 2012). The negative impact of the automated thresholding using a relatively rigid thresholding system, such as the one available in Assess (Lamari 2002), was highlighted (Bock et al 2009).…”