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Optical Engineering Applications for Smart Agriculture

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Dear Colleagues,

Over the next few decades, it is anticipated that the need for food security and safety will significantly grow in tandem with the growing global population. Therefore, there is a need to adopt novel, environmentally friendly, and rapid technologies to enhance the quantity, quality, and safety of agriproducts as current technologies will not be able to address the world's future needs. Optical-based technologies such as lasers, imaging, spectroscopy and spectral imaging have proven to have the potential to offer practical solutions for promoting sustainable agriculture.

This Special Issue of Sustainability is intended to serve as a platform for sharing research findings and insights in "Optical Engineering Applications for Smart Agriculture".

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Application of optical-based techniques in enhancing crop germination, growth and development.
Application of optical-based techniques in tracking the growth of agri-food products.
Application of optical-based techniques in assessing the health of agri-food products.
Application of optical-based techniques in pest monitoring.
Application of optical-based techniques in improving the safety of agri-food products.
Application of optical-based techniques in precision irrigation.
Application of optical-based techniques in field resource management and smart farming.

Dr. Mohammad Nadimi
Guest Editor

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Keywords

optical techniques
sustainable agriculture
smart farming
agricultural resource management
laser biostimulation
LIDARS
imaging
spectroscopy
hyperspectral imaging

Published Papers (2 papers)

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Research

24 pages, 12921 KiB

Open AccessArticle

A Machine-Learning Approach for Automatic Grape-Bunch Detection Based on Opponent Colors

by Vittoria Bruni, Giulia Dominijanni and Domenico Vitulano

Sustainability 2023, 15(5), 4341; https://doi.org/10.3390/su15054341 - 28 Feb 2023

Cited by 1 | Viewed by 1415

Abstract

This paper presents a novel and automatic artificial-intelligence (AI) method for grape-bunch detection from RGB images. It mainly consists of a cascade of support vector machine (SVM)-based classifiers that rely on visual contrast-based features that, in turn, are defined according to grape bunch color visual perception. Due to some principles of opponent color theory and proper visual contrast measures, a precise estimate of grape bunches is achieved. Extensive experimental results show that the proposed method is able to accurately segment grapes even in uncontrolled acquisition conditions and with limited computational load. Finally, such an approach requires a very small number of training samples, making it appropriate for onsite and real-time applications that are implementable on smart devices, usable and even set up by winemakers. Full article

(This article belongs to the Special Issue Optical Engineering Applications for Smart Agriculture)

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11 pages, 643 KiB

Open AccessArticle

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (Linum usitatissimum)

by Mohammad Nadimi, Georgia Loewen, Pankaj Bhowmik and Jitendra Paliwal

Sustainability 2022, 14(19), 12183; https://doi.org/10.3390/su141912183 - 26 Sep 2022

Cited by 10 | Viewed by 1771

Abstract

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 °C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm², respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds’ root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth. Full article

(This article belongs to the Special Issue Optical Engineering Applications for Smart Agriculture)

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