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Application and Framework Development for Agriculture
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Application and Framework Development for Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 9535

Special Issue Editors

Prof. Dr. José Manuel Cadenas

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
Interests: data mining and analytics; soft computing; imprecise data; machine learning and inference with uncertainty and imprecision, (fuzzy) optimization and decision analysis; metaheuristics
Dr. María Carmen Garrido

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
Interests: data preprocessing; machine learning; imprecise and uncertain data treatment; machine learning in fuzzy framework; time series data management
Dr. Raquel Martínez España

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain
Interests: data mining; soft computing; uncertainty/imprecision treatment; classification; regression; decision tree; ensembles

Special Issue Information

Dear Colleagues,

Advances in technology are forcing change in many areas of the real world. In general, users and businesses can have access to information and services anywhere and at any time through a variety of devices. Nowadays, through mobile devices, a large number of users can be reached through effective communication. Increasingly, users want to be connected to obtain useful information in real time. New sensor technologies, artificial intelligence (AI), and machine learning (ML) are creating a new middle layer between the user and systems to efficiently solve complex problems and everyday issues. This is enabling successful results in precision agriculture, efficient water management, and early detection of pests and frost, among others.

If we focus on the agricultural sector, farmers belong to a group of users who are far away from both other members of their group and the information and decision centers, so it is even more important to develop applications and services such that they can also take advantage of the technological development of mobile devices. Farmers could benefit from the use of software applications with specialized information about crop development and techniques to improve decision making in crop management, implement changes in their production systems, etc.

Therefore, this Special Issue attempts to bring together research and review articles on different software applications and/or application frameworks for precision agriculture, with special emphasis on software applications based on AI and ML.

Prof. Dr. José Manuel Cadenas
Dr. María Carmen Garrido
Dr. Raquel Martínez España
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. Sensors 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 2600 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

  • precision agriculture
  • artificial intelligence
  • machine learning
  • applications
  • recommender systems
  • predictor systems
  • control systems

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Published Papers (5 papers)

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Research

20 pages, 4152 KiB  
Article
Precision Agriculture Applied to Harvesting Operations through the Exploitation of Numerical Simulation
by Federico Cheli, Ahmed Khaled Mohamed Abdelaziz, Stefano Arrigoni, Francesco Paparazzo and Marco Pezzola
Sensors 2024, 24(4), 1214; https://doi.org/10.3390/s24041214 - 14 Feb 2024
Viewed by 936
Abstract
When it comes to harvesting operations, precision agriculture needs to consider both combine harvester technology and the precise execution of the process to eliminate harvest losses and minimize out-of-work time. This work aims to propose a complete control framework defined by a two-layer-based [...] Read more.
When it comes to harvesting operations, precision agriculture needs to consider both combine harvester technology and the precise execution of the process to eliminate harvest losses and minimize out-of-work time. This work aims to propose a complete control framework defined by a two-layer-based algorithm and a simulation environment suitable for quantitative harvest loss, time, and consumption analyses. In detail, the path-planning layer shows suitable harvesting techniques considering field boundaries and irregularities, while the path-tracking layer presents a vision-guided Stanley Lateral Controller. In order to validate the developed control framework, challenging driving scenarios were created using IPG-CarMaker software to emulate wheat harvesting operations. Results showed the effectiveness of the designed controller to follow the reference trajectory under regular field conditions with zero harvest waste and minimum out-of-work time. Whereas, in presence of harsh road irregularities, the reference trajectory should be re-planned by either selecting an alternative harvesting method or overlapping the harvester header by some distance to avoid missing crops. Quantitative and qualitative comparisons between the two harvesting techniques as well as a relationship between the level of irregularities and the required overlap will be presented. Eventually, a Driver-in-the-loop (DIL) framework is proposed as a methodology to compare human and autonomous driving. Full article
(This article belongs to the Special Issue Application and Framework Development for Agriculture)
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20 pages, 2864 KiB  
Article
Low-Cost Optical Sensors for Soil Composition Monitoring
by Francisco Javier Diaz, Ali Ahmad, Lorena Parra, Sandra Sendra and Jaime Lloret
Sensors 2024, 24(4), 1140; https://doi.org/10.3390/s24041140 - 9 Feb 2024
Cited by 4 | Viewed by 2400
Abstract
Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time [...] Read more.
Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification. Full article
(This article belongs to the Special Issue Application and Framework Development for Agriculture)
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23 pages, 8527 KiB  
Article
Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming
by Juan Felipe Jaramillo-Hernández, Vicente Julian, Cedric Marco-Detchart and Jaime Andrés Rincón
Sensors 2024, 24(3), 937; https://doi.org/10.3390/s24030937 - 31 Jan 2024
Cited by 2 | Viewed by 1862
Abstract
In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer [...] Read more.
In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things. Full article
(This article belongs to the Special Issue Application and Framework Development for Agriculture)
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16 pages, 627 KiB  
Article
A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems
by Jose M. Cadenas, M. Carmen Garrido and Raquel Martínez-España
Sensors 2023, 23(6), 3038; https://doi.org/10.3390/s23063038 - 11 Mar 2023
Cited by 7 | Viewed by 1762
Abstract
Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together [...] Read more.
Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal. Full article
(This article belongs to the Special Issue Application and Framework Development for Agriculture)
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14 pages, 3197 KiB  
Article
Implementing a Compression Technique on the Progressive Contextual Excitation Network for Smart Farming Applications
by Setya Widyawan Prakosa, Jenq-Shiou Leu, He-Yen Hsieh, Cries Avian, Chia-Hung Bai and Stanislav Vítek
Sensors 2022, 22(24), 9717; https://doi.org/10.3390/s22249717 - 12 Dec 2022
Cited by 2 | Viewed by 1713
Abstract
The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority [...] Read more.
The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority of the deep learning model on the smart farming application, called Progressive Contextual Excitation Network (PCENet), has also been studied in our recent study to classify cocoa bean images. However, the assessment of the computational time on the PCENet model shows that the original model is only 0.101s or 9.9 FPS on the Jetson Nano as the edge platform. Therefore, this research demonstrates the compression technique to accelerate the PCENet model using pruning filters. From our experiment, we can accelerate the current model and achieve 16.7 FPS assessed in the Jetson Nano. Moreover, the accuracy of the compressed model can be maintained at 86.1%, while the original model is 86.8%. In addition, our approach is more accurate than ResNet18 as the state-of-the-art only reaches 82.7%. The assessment using the corn leaf disease dataset indicates that the compressed model can achieve an accuracy of 97.5%, while the accuracy of the original PCENet is 97.7%. Full article
(This article belongs to the Special Issue Application and Framework Development for Agriculture)
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