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Applied Sciences
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Recent Advances in Precision Farming and Digital Agriculture
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Recent Advances in Precision Farming and Digital Agriculture

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 12597

Special Issue Editors

Prof. Dr. Michele Raffaelli

E-Mail Website1 Website2
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy
Interests: machines for soil tillage; conservation and no tillage; machines for physical weed control; soil disinfection with physical methods; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Fontanelli

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto, 80, 56124 Pisa, Italy
Interests: farm mechanization and farm machinery; precision agriculture; conservation agriculture; nonchemical weed control; machine for turfgrass and landscape management
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Antichi

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
Interests: sustainable agricultural systems; agroecology; cover crops; intercropping; agroforestry; conservation agriculture; organic farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital agriculture likely represents the new frontier of precision farming. The collection, use and distribution of data to boost farmer activity is now a real and applicable reality. These technologies are useful: not only do they support farming activities, but they also assist in food chain activities and operations related to the maintenance of urban green areas and forestry.

Digital technologies can help “intelligent” machines used in precision farming to optimize their efficiency. The final aim of digital and precision farming is to save costs, time, labor and inputs within sustainable farming systems. Moreover, digital technologies help different machines and devices to share data collected from specific sensors. The farmers, accessing to these information, can be helped in the many decision processes.

In this Special Issue, all contributions regarding innovative technologies and machines for digital and precision agriculture are welcome, including all the agricultural, food chain, urban green areas and forestry applications. Manuscripts describing software, sensors, robotic, automation and artificial intelligence applications are also welcome. Thus, we invite experts and researchers to contribute with original research, reviews and opinion pieces covering the topics of this Special Issue.

Prof. Dr. Michele Raffaelli
Dr. Marco Fontanelli
Dr. Daniele Antichi
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. Applied Sciences 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

  • artificial intelligence
  • internet of things
  • agri-technology
  • automation
  • robotics
  • input reduction
  • sustainable farming systems
  • variable rate applications
  • sensors

Published Papers (8 papers)

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Research

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27 pages, 12499 KiB  
Article
Incorporating Multi-Temporal Remote Sensing and a Pixel-Based Deep Learning Classification Algorithm to Map Multiple-Crop Cultivated Areas
by Xue Wang, Jiahua Zhang, Xiaopeng Wang, Zhenjiang Wu and Foyez Ahmed Prodhan
Appl. Sci. 2024, 14(9), 3545; https://doi.org/10.3390/app14093545 - 23 Apr 2024
Viewed by 205
Abstract
The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term [...] Read more.
The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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19 pages, 35881 KiB  
Article
Harnessing Digital Twins for Agriculture 5.0: A Comparative Analysis of 3D Point Cloud Tools
by Paula Catala-Roman, Enrique A. Navarro, Jaume Segura-Garcia and Miguel Garcia-Pineda
Appl. Sci. 2024, 14(5), 1709; https://doi.org/10.3390/app14051709 - 20 Feb 2024
Viewed by 784
Abstract
Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review [...] Read more.
Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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22 pages, 6033 KiB  
Article
An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines
by Antonios Morellos, Konstantinos Dolaptsis, Georgios Tziotzios, Xanthoula Eirini Pantazi, Dimitrios Kateris, Remigio Berruto and Dionysis Bochtis
Appl. Sci. 2024, 14(3), 1049; https://doi.org/10.3390/app14031049 - 26 Jan 2024
Viewed by 615
Abstract
Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things [...] Read more.
Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things approach. An edge device, a Raspberry Pi 4 equipped with an RGB camera, is utilized to detect diseases in grapevine plants. Two lightweight deep learning models, MobileNet V2 and EfficientNet B0, were trained using a transfer learning technique on commercially available online dataset, then deployed and validated on field-site in an organic winery. The models’ performance was further enhanced using semantic segmentation with the Mobile-UNet algorithm. Results were reported through a web service using FastAPI. Both models achieved high training accuracies exceeding 95%, with MobileNet V2 slightly outperforming EfficientNet B0. During validation, MobileNet V2 achieved an accuracy of 94%, compared to 92% for EfficientNet B0. In terms of IoT deployment, MobileNet V2 exhibits faster inference time (330 ms) compared to EfficientNet B0 (390 ms), making it the preferred model for online deployment. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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18 pages, 4832 KiB  
Article
A Study on Analyses of the Production Data of Feed Crops and Vulnerability to Climate Impacts According to Climate Change in Republic of Korea
by MoonSun Shin, Seonmin Hwang, Junghwan Kim, Byungcheol Kim and Jeong-Sung Jung
Appl. Sci. 2023, 13(20), 11603; https://doi.org/10.3390/app132011603 - 23 Oct 2023
Viewed by 1003
Abstract
According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply [...] Read more.
According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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16 pages, 4133 KiB  
Article
Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
by Mino Sportelli, Orly Enrique Apolo-Apolo, Marco Fontanelli, Christian Frasconi, Michele Raffaelli, Andrea Peruzzi and Manuel Perez-Ruiz
Appl. Sci. 2023, 13(14), 8502; https://doi.org/10.3390/app13148502 - 23 Jul 2023
Cited by 12 | Viewed by 2805
Abstract
The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. [...] Read more.
The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP_0.5 and mAP_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP_0.5 (0.9795), and mAP_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for further enhancements to address the challenges impeding accurate weed detection. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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14 pages, 1712 KiB  
Article
Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
by Sofia Matilde Luglio, Mino Sportelli, Christian Frasconi, Michele Raffaelli, Lorenzo Gagliardi, Andrea Peruzzi, Veronica Fortini, Marco Volterrani, Simone Magni, Lisa Caturegli, Giuliano Sciusco and Marco Fontanelli
Appl. Sci. 2023, 13(13), 7852; https://doi.org/10.3390/app13137852 - 04 Jul 2023
Cited by 2 | Viewed by 850
Abstract
Robotic solutions and technological advances for turf management demonstrated excellent results in terms of quality, energy, and time consumption. Two battery-powered autonomous mowers (2 WD and 4 WD) with random patterns were evaluated according to different trampling levels (control, low, medium, high) on [...] Read more.
Robotic solutions and technological advances for turf management demonstrated excellent results in terms of quality, energy, and time consumption. Two battery-powered autonomous mowers (2 WD and 4 WD) with random patterns were evaluated according to different trampling levels (control, low, medium, high) on a typical warm season turfgrass at the DAFE, University of Pisa, Italy. Data on the percentage of area mowed, the distance traveled, the number of passages, and the number of intersections were collected through RTK devices and processed by a custom-built software (1.8.0.0). The main quality parameters of the turfgrass were also analyzed by visual and instrumental assessments. Soil penetration resistance was measured through a digital penetrometer. The efficiency significantly decreased as the trampling level increased (from 0.29 to 0.11). The over-trampled areas were mainly detected by the edges (on average for the medium level: 18 passages for the edges vs. 14 in the central area). The trampling activity caused a reduction in turf height (from about 2.2 cm to about 1.5 cm). The energy consumption was low and varied from 0.0047 to 0.048 kWh per cutting session. Results from this trial demonstrated suitable quality for a residential turf of the Mediterranean area (NDVI values from 0.5 to 0.6), despite the over-trampling activity. Soil penetration data were low due to the reduced weight of the machines, but slightly higher for the 4 WD model (at 5 cm of depth, about 802 kPa vs. 670 kPa). Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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30 pages, 3519 KiB  
Article
Information and Communication Technologies and Agricultural Production: New Evidence from Africa
by Robert Ugochukwu Onyeneke, Daniel Adu Ankrah, Richmond Atta-Ankomah, Fred Fosu Agyarko, Chinenye Judith Onyeneke and Jalil Ghassemi Nejad
Appl. Sci. 2023, 13(6), 3918; https://doi.org/10.3390/app13063918 - 19 Mar 2023
Cited by 11 | Viewed by 3263
Abstract
While information and communication technologies (ICT) have proven to be useful in boosting agricultural production and productivity, regardless of the geographical location, much of the discussion on ICT and their impact focus on the global north, with deficient literature on the global south. [...] Read more.
While information and communication technologies (ICT) have proven to be useful in boosting agricultural production and productivity, regardless of the geographical location, much of the discussion on ICT and their impact focus on the global north, with deficient literature on the global south. The limited account of the global south shows mixed conclusions on the impact of information and communication technologies on agricultural production, with most studies focusing on crop production, as a proxy for agricultural production, leaving out livestock production. Animated by this concern, this article explores the impact of ICTs on agricultural production (crop and livestock) in Africa using panel data from 32 African countries and the panel autoregressive distributed lag model as the estimation technique. We find that individuals using internet significantly increased crop production in the long run. Specifically, a percentage increase in internet patronage increases crop production by 0.071% but significantly decreases the livestock production index, both in the short and long run. Mobile phone subscriptions had a significant negative impact on crop production in the long run but had a significant positive impact on livestock production in the long run. Fixed phone subscriptions significantly increased crop production in the long run but significantly decreased livestock production index in the long run. The findings show bidirectional causality between crop production and internet patronage, livestock production and individuals using internet, crop production and mobile cellular subscription, crop production and net national income, and rural population and both crop and livestock production. We recommend that governments in Africa increase funding investment in digital technologies to foster increased agricultural production while addressing structural challenges that constrain increased access to digital agricultural technologies. It might be useful if governments in sub-Saharan Africa (SSA) incentivize the telecommunication companies to extend digital coverage to rural areas through tax rebates and holidays to encourage rural inclusion in the digital space to bridge the digital divide. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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Review

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16 pages, 880 KiB  
Review
Algorithms and Models for Automatic Detection and Classification of Diseases and Pests in Agricultural Crops: A Systematic Review
by Mauro Francisco, Fernando Ribeiro, José Metrôlho and Rogério Dionísio
Appl. Sci. 2023, 13(8), 4720; https://doi.org/10.3390/app13084720 - 09 Apr 2023
Cited by 3 | Viewed by 2080
Abstract
Plant diseases and pests significantly influence food production and the productivity and economic profitability of agricultural crops. This has led to great interest in developing technological solutions to enable timely and accurate detection. This systematic review aimed to find studies on the automation [...] Read more.
Plant diseases and pests significantly influence food production and the productivity and economic profitability of agricultural crops. This has led to great interest in developing technological solutions to enable timely and accurate detection. This systematic review aimed to find studies on the automation of processes to detect, identify and classify diseases and pests in agricultural crops. The goal is to characterize the class of algorithms, models and their characteristics and understand the efficiency of the various approaches and their applicability. The literature search was conducted in two citation databases. The initial search returned 278 studies and, after removing duplicates and applying the inclusion and exclusion criteria, 48 articles were included in the review. As a result, seven research questions were answered that allowed a characterization of the most studied crops, diseases and pests, the datasets used, the algorithms, their inputs and the levels of accuracy that have been achieved in automatic identification and classification of diseases and pests. Some trends that have been most noticed are also highlighted. Full article
(This article belongs to the Special Issue Recent Advances in Precision Farming and Digital Agriculture)
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