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Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 14033

Special Issue Editors

Dr. Paolo Liberati

E-Mail
Guest Editor
Agricultural and Food Sciences Department (DISTAL), University of Bologna - Viale G. Fanin 50, Bologna, Italy
Interests: climatization; computer science; biogas; animal welfare; automation; simulation models; animal housing; precision livestock farming
Prof. Dr. Valda Rondelli

E-Mail Website
Assistant Guest Editor
Agricultural and Food Sciences Department (DISTAL), University of Bologna - Viale G. Fanin 50, Bologna, Italy
Interests: safety of agricultural machines; tractor rollover sprayers; agricultural and forestry tractor; filtered and pressurized cabs
Dr. Alberto Assirelli

E-Mail Website1 Website2
Assistant Guest Editor
Council for Agricultural Research and Economics (CREA), Research Centre for Engineering and Agro-Food Processing, via della Pascolare, 16, Monterotondo Scalo, 00015 Rome, Italy
Interests: precision organic farming; mechanical and physical wedding; VRT approach in organic farming

Special Issue Information

Dear Colleagues,

Sustainable Agriculture has many declinations depending on the actors involved or the specific aspects to be preserved and protected. Sustainability mainly refers to environmental and economic aspects, although the social aspects, addressed to improve life quality of the operators, together with their safety and health at work, are nowadays increasingly emphasized. Currently these are three subjects requiring more and more attention in reason of the climate changes, always in progress and, unfortunately, prone to cause extreme difficult situations in the future of our world.

In the context of this Special Issue, the aim is to highlight computer applications, sensors and electronics leading to improve the sustainability of the agricultural processes, reducing the environmental impact or increasing its resilience, as well as improving animal welfare.

Contributions using both the Precision farming approach (Precision Agriculture and Precision livestock farming) and the computer analysis and modeling (i.e. CFD, deep learning, emissions) are invited to disseminate new ideas and concepts addressed to enhance all sustainability declinations in agriculture, including organic farming approaches.

Dr. Paolo Liberati
Guest Editor

Prof. Valda Rondelli
Dr. Alberto Assirelli
Assistant Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainability
  • environmental impact
  • computer science
  • sensors
  • electronics
  • automation
  • simulation models
  • precision farming
  • precision organic farming
  • mechanical and physical wedding
  • vrt approach in organic farming
  • vrt soil improver spreader
  • soil precision tillage in organic farming low input
  • precision spreading
  • reduced soil tillage in conservative agriculture
  • animal welfare
  • safety of agricultural machines
  • tractor rollover
  • sprayers
  • agricultural and forestry tractor
  • filtered and pressurized cabs
  • animal housing climatization and climate change resilience

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Published Papers (5 papers)

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Research

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12 pages, 4772 KiB  
Article
Multi-Parametric Approach to Management Zone Delineation in a Hazelnut Grove in Italy
by Roberta Martelli, Vincenzo Civitarese, Lorenzo Barbanti, Abid Ali, Giulio Sperandio, Andrea Acampora, Davide Misturini and Alberto Assirelli
Sustainability 2023, 15(13), 10106; https://doi.org/10.3390/su151310106 - 26 Jun 2023
Cited by 6 | Viewed by 1186
Abstract
The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in [...] Read more.
The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs. Full article
(This article belongs to the Special Issue Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production)
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16 pages, 3165 KiB  
Article
An Active Drying Sensor to Drive Dairy Cow Sprinkling Cooling Systems
by Paolo Liberati
Sustainability 2023, 15(12), 9384; https://doi.org/10.3390/su15129384 - 10 Jun 2023
Viewed by 2020
Abstract
The use of sprinkling with ventilation to cool dairy cows is considered an appropriate practice to reduce the negative effects of heat stress. However, due to climate change, water will increasingly become a limited resource, so we need to make water use more [...] Read more.
The use of sprinkling with ventilation to cool dairy cows is considered an appropriate practice to reduce the negative effects of heat stress. However, due to climate change, water will increasingly become a limited resource, so we need to make water use more and more efficient. For this purpose, an active drying sensor has been developed in order to time the sprinkling cooling system. The sensor reproduces the thermal response of a cow, considering both sensible and latent heat exchange, and is located in the feeding alley, about two meters above the floor. This allows the fabric of the sensor (simulating the fur) to be wetted by the sprinkler, and blown by the fan. The water content of the sensor fabric during the drying time is monitored by measuring its electrical conductivity, allowing the estimation of the time the fur becomes dry. Another two specifically designed instruments are presented, the first to estimate the fur’s water content after spraying, and the second to detect the time the fur became dry. Sensor output, interpreted through a simplified model, gave a predicted drying time with an error ranging between −11.4% and +14.8% (R2 = 0.789). In the commercial barn where the experiments were conducted, the use of the sensor allowed an estimated reduction in water consumption of about 57%, with respect to the fixed timing normally used. As a perspective, the sensor could be used to assess cows’ heat stress level. Full article
(This article belongs to the Special Issue Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production)
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14 pages, 2849 KiB  
Article
Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars
by Kadir Sabanci
Sustainability 2023, 15(5), 4443; https://doi.org/10.3390/su15054443 - 2 Mar 2023
Cited by 12 | Viewed by 2451
Abstract
In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image [...] Read more.
In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification. Full article
(This article belongs to the Special Issue Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production)
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12 pages, 16497 KiB  
Article
Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard
by Alberto Assirelli, Elio Romano, Carlo Bisaglia, Enrico Maria Lodolini, Davide Neri and Massimo Brambilla
Sustainability 2021, 13(15), 8266; https://doi.org/10.3390/su13158266 - 23 Jul 2021
Cited by 13 | Viewed by 2665
Abstract
The evaluation of the canopy in orchard cultivation is a key aspect for the main cultivation techniques, such as pruning, thinning, harvesting, production and improved fruit quality. The possibility of having a periodic screening of the state of development of the vegetation can [...] Read more.
The evaluation of the canopy in orchard cultivation is a key aspect for the main cultivation techniques, such as pruning, thinning, harvesting, production and improved fruit quality. The possibility of having a periodic screening of the state of development of the vegetation can be of practical support to growers. Research on the application of precision agriculture has provided tools for reading and interpreting crops, and the resulting information is potentially useful. Many of the systems under study provide after monitoring information processing systems that reduce the timeliness of intervention. Especially in intensive systems such as olive groves, knowing the precise intervention points is often essential. In the present work, a multi-parameter instrument was used for field monitoring on the agricultural tractor to analyse the canopy. The system allows measuring various indicators such as height and density of the canopy and the temperature and humidity of the ambient air and at the leaf level. The first evaluation of the data made it possible to identify areas with greater vegetative concentration and greater or lesser development. The system made it possible to identify with good approximation the homogeneous areas, based on the Canopy Index (CI) evaluation to be subjected to subsequent and specific management efforts, dividing them into low, ordinary, and high vegetative growth. The results highlight the possibility of directly combining operators able to intervene with the same passage, selecting based on differences in growth, typical varietal specificities, and areas of deficient development or that are affected by plant diseases, confirming the objective of defining the areas of the orchard that require different management and workload techniques. Full article
(This article belongs to the Special Issue Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production)
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Review

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16 pages, 4647 KiB  
Review
A Review of Current and Historical Research Contributions to the Development of Ground Autonomous Vehicles for Agriculture
by Valda Rondelli, Bruno Franceschetti and Dario Mengoli
Sustainability 2022, 14(15), 9221; https://doi.org/10.3390/su14159221 - 27 Jul 2022
Cited by 17 | Viewed by 4535
Abstract
In this study, a comprehensive overview of the available autonomous ground platforms developed by universities and research groups that were specifically designed to handle agricultural tasks was performed. As cost reduction and safety improvements are two of the most critical aspects for farmers, [...] Read more.
In this study, a comprehensive overview of the available autonomous ground platforms developed by universities and research groups that were specifically designed to handle agricultural tasks was performed. As cost reduction and safety improvements are two of the most critical aspects for farmers, the development of autonomous vehicles can be of major interest, especially for those applications that are lacking in terms of mechanization improvements. This review aimed to provide a literature evaluation of present and historical research contributions toward designing and prototyping agricultural ground unmanned vehicles. The review was motivated by the intent to disseminate to the scientific community the main features of the autonomous tractor named BOPS-1960, which was conceived in the 1960s at the Alma Mater Studiorum University of Bologna (UNIBO). Jointly, the main characteristics of the modern DEDALO unmanned ground vehicle (UGV) for orchard and vineyard operations that was designed recently were evaluated. The basic principles, technology and sensors used in the two UNIBO prototypes are described in detail, together with an analysis of UGVs for agriculture conceived in recent years by research centers all around the world. Full article
(This article belongs to the Special Issue Less Impact, More Resilience and Welfare: Computer and Electronics to Improve the Sustainability in Agriculture Production)
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