EditorialSensors and Embedded Systems in Agriculture and Food Analysis

Marco Grossi ,1 Annachiara Berardinelli,2 Edward Sazonov ,3 Wesley Beccaro,4

and Martin Omaña 1

1Department of Electrical Energy and Information Engineering, University of Bologna, Bologna, Italy2Department of Agricultural and Food Science, University of Bologna, Cesena, Italy3Department of Electrical and Computer Engineering, University of Alabama, Alabama, USA4Department of Electronic Systems Engineering, University of São Paulo, São Paulo, Brazil

Correspondence should be addressed to Marco Grossi; marco.grossi8@unibo.it

Received 9 December 2018; Accepted 9 December 2018; Published 9 January 2019

Copyright © 2019 Marco Grossi et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

The continuous advance in sensors and sensing systems has astrong impact in agriculture and food production.

Food is routinely screened to assess quality (such asphysical appearance and organoleptic properties) and safety(absence of health threatening pathogens and chemicalcompounds). These tests are usually carried out in laboratoryby skilled personnel, thus resulting in delayed response andhigh costs for the analysis. On the other hand, the availabilityof transduction techniques (such as electrical impedancespectroscopy, visible and near-infrared optical spectroscopy,fluorescence spectroscopy, and image processing) allows thedesign of low-cost embedded sensor systems for quickin-the-field analysis with benefits in terms of lower cost,shorter time response, and, in the end, more frequentscreening and improved product quality.

Similarly, the introduction of sensor technologies inagriculture has led to the transition from standard farms,where activities are almost entirely carried out byhumans, to “smart farms,” where activities are automa-tized, critical parameters are timely monitored by networksof distributed sensors and cameras, information is sharedusing high-speed wireless communication technologies,and energy is scavenged from natural sources (solar,thermal, etc.). Moreover, the ever-increasing diffusion ofmodern mobile phones, merging strong computation capa-bility with fast wireless communication, promotes even morethe transition to the new “smart farms” in the paradigm ofInternet of Things (IoT).

This special issue presents six papers that have beenpublished after two rounds of rigorous peer review.

In the paper “Urban Lawn Monitoring in Smart CityEnvironments,” J. Marín et al. propose an Arduino-basedsystem with a camera mounted on a drone that enables tomonitor the state of the grass in urban lawns and conse-quently to optimize the irrigation regime. In the proposedstrategy, the drone periodically flies over a garden and takespictures of the grass. The pictures are then processed withan algorithm that classifies the grass into three categories,accordingly to its quality (thus to the need of irrigation).The authors apply their drone-based strategy to monitorgardens with different sizes to validate its cost and applica-bility, as well as to compare its pros and cons over alter-native monitoring systems (mounted on mall autonomousvehicles). The experimental results demonstrate that for largegardens (bigger than 1000m2), the proposed strategyachieves a significantly lower monitoring time.

In the paper “The State-of-the-Art of Knowledge-Intensive Agriculture: A Review on Applied Sensing Systemsand Data Analytics,” B. Basnet and J. Bang reviewedexisting sensors and data analytics techniques used in dif-ferent areas of agriculture. The authors classified agricul-ture into five categories and reviewed the state-of-the-arttechnology in practice and ongoing research in each ofthese areas. The authors also discussed current and futurechallenges and provided their views on how such issuescan be addressed.

HindawiJournal of SensorsVolume 2019, Article ID 6808674, 2 pageshttps://doi.org/10.1155/2019/6808674

In the paper “On-the-Go Grapevine Yield EstimationUsing Image Analysis and Boolean Model,” B. Millan et al.proposed a methodology for image-based automated estima-tion of vineyard yields. The paper suggests use of a Booleanmodel to tackle the problem of occlusions in the imagesand evaluates the model’s performance on three differentdatasets. The number of berries in a cluster was estimatedwith a root mean square error (RMSE) of 20 and a coefficientof determination (R2) of 0.80. The mass of berries on theimages of the vines was estimated with 310 grams of meanerror and R2 = 0 81 (manually taken images) and 610 gramsof mean error per segment (three vines) and R2 = 0 78 fromimages taken by an automatic camera.

In the paper “The Development of an IntelligentMonitoring System for Agricultural Inputs Basing onDBN-SOFTMAX,” L. Yang et al. proposed a system basedon deep belief network (DBN) using a SOFTMAX classifierfor the traceability of chemical fertilizers and pesticides inagricultural products. The system features sensor nodesperforming measurement of pH, electrical conductivity andmoisture, and a LoRa wireless communication module thattransfers the measured data to a cloud server for furtherprocessing. Tests have been carried out on six agriculturalinputs (three chemical fertilizers and three pesticides), andthe results have shown how the proposed system providesan accuracy of 98.5%.

In the paper “Applicability of a 3D Laser Scanner forCharacterizing the Spray Distribution Pattern of an Air-Assisted Sprayer,” F. J. García-Ramos et al. presented anoriginal work conducted by using a three-dimensional (3D)laser technology for the assessment of the performance ofair-assisted spraying in fruit orchards. The authors developeda static test using an air-assisted sprayer equipped with twoaxial fans (front and back) with opposing directions ofrotation. Two critical criteria were considered: the depositionof the product as a function of distance and the productdistribution in the vicinity of the machine. The main resultsevidenced that measurements carried out by using the lasersensor allowed the quantification of the maximum distanceof deposition of the product and the quantification of theamount of products applied in different areas in the vicinityof the sprayer.

In the paper “Monitoring and Control Systems inAgriculture Using Intelligent Sensor Techniques: A Reviewof the Aeroponic System,” I. A. Lakhiar et al. presented areview on the use of wireless sensor network (WSN) in aero-ponic cultivation. The authors discussed the advantages ofaeroponics, a cultivation system where plant roots arehanged in the darkness in a growth chamber and providedwith a nutrient mist in replacement of the soil, in terms ofbetter control and possibility to investigate the effects ofdifferent nutrients on plant growth. A discussion on theproblems faced by the application of aeroponics in terms ofattention required by the grower to timely detect failures inthe atomization nozzles and monitor critical parametershas been also presented. The authors discussed how theuse of WSNs in aeroponics can effectively solve thesedrawbacks, thus helping the diffusion of the technique tofarmers and scientists.

Conflicts of Interest

The guest editorial team as a whole declares that no conflictof interest or private agreement with companies exists.

Marco GrossiAnnachiara Berardinelli

Edward SazonovWesley BeccaroMartin Omaña

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