Special Issue "Agriculture 4.0: From Precision Agriculture to Smart Farming"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 30 January 2022.

Special Issue Editors

Prof. Dr. Pasquale Catalano
E-Mail Website
Guest Editor
Department of Agricultural, Environmental and Food Sciences, University of Molise, 86100 Campobasso CB, Italy
Interests: sustainable development of smart agriculture; remote sensing (processing of satellite images and/or from drones; IoT and data analysis, etc.) for precision agriculture and industry 4.0 in the whole food production chain; food processing plant automation and optimization; energy saving and natural resources optimization
Prof. Dr. Antonia Tamborrino
E-Mail Website
Guest Editor
Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
Interests: innovation and optimization of the agro-food industry plant; olive oil extraction plant; process efficiency; new technology; by-products equipment and plant; processes settings and control
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Special Issue Information

Dear Colleagues,

Agriculture 4.0, the natural evolution of precision agriculture, makes it possible to face these challenges by allowing an intelligent and controlled application of inputs (water, nutrients, pesticides, energy), optimized management of production from field to table, integration of different technologies in agriculture, etc. The use of software, applications, networks of sensors, all supported by Internet of Things (IoT) technologies, make data easily manageable and accessible. This allows to accurately provide and analyse information in real time, allowing the automation of agricultural production and decision-making processes. For example, at the current state of knowledge, typical consolidated technologies of precision agriculture allow farmers to program the variable distribution of inputs according to the spatial and temporal variability of crops. On the other hand, the possibilities offered by the latest generation technologies allow for an integrated communication between all the tools present in the farm with a high level data accessibility between the components of the supply chain: machines, equipment, farms, customers, dealers and institutions, etc. Therefore, the agriculture in the future will increasingly use sophisticated technologies such as robots, field sensors, aerial imagery, GPS technology, software completely interconnected by IoT networks allowing farms to be more profitable, efficient, safer and more environmentally friendly. Among the topics we highlight:

History of Precision Agriculture, Sensing Technology for Precision Farming (satellite, aerial, UAV, proximal sensing platforms, etc.), Data Processing and Utilization in Precision Agriculture, Image Processing, Control of Precision Agriculture Production, Big data analysis applied to precision agriculture, Intelligent Agricultural Machinery and Field Robots, Traceability Smart Agriculture, Precision Farming Economics.

Prof. Dr. Pasquale Catalano
Prof. Dr. Antonia Tamborrino
Guest Editors

Manuscript Submission Information

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Keywords

  • history of precision agriculture
  • sensing technology for precision farming (satellite, aerial, UAV, proximal sensing platforms, etc.)
  • data processing and utilization in precision agriculture
  • image processing
  • control of precision agriculture production
  • big data analysis applied to precision agriculture
  • intelligent agricultural machinery and field robots
  • traceability smart agriculture
  • precision farming economics

Published Papers (1 paper)

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Research

Article
Design of a Sweet Potato Transplanter Based on a Robot Arm
Appl. Sci. 2021, 11(19), 9349; https://doi.org/10.3390/app11199349 - 08 Oct 2021
Viewed by 211
Abstract
Traditional sweet potato transplanters have the problem of seedling leakage and can only accomplish one transplantation method at a time, which does not meet the requirements of complex planting terrain that requires multiple transplantation methods. Therefore, this paper proposes a design for a [...] Read more.
Traditional sweet potato transplanters have the problem of seedling leakage and can only accomplish one transplantation method at a time, which does not meet the requirements of complex planting terrain that requires multiple transplantation methods. Therefore, this paper proposes a design for a crawler-type sweet potato transplanting machine, which can accomplish a variety of transplanting trajectories and conduct automatic replanting. The machine has a transplanting piece and a replanting piece. The transplanting piece completes the transplanting action through a transplanting robot arm, and the replanting piece detects the transplanting status by deep learning. The mathematical model of the transplanting robot arm is built, and the transplanting trajectory is inferred from the inverse kinematics model of the transplanting robot. In the replanting piece, a target detection network is used to detect the transplanting status. The DBIFPN structure and the CBAM_Dense attention mechanism are proposed to improve the accuracy of the target detection of sweet potato seedlings. The experiment showed that the transplanting robot arm can transplant sweet potatoes in horizontal and vertical methods, and the highest transplanting qualification rate is 96.8%. Compared with the use of the transplanting piece alone, the leakage rate of the transplanting–replanting mechanism decreased by 5.2%. These results provide a theoretical basis and technical support for the research and development of sweet potato transplanters. Full article
(This article belongs to the Special Issue Agriculture 4.0: From Precision Agriculture to Smart Farming)
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