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New Developments in Smart Farming Applied in Sustainable Agriculture, 2nd Edition

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 5406

Special Issue Editors

Prof. Dr. Katarzyna Pentoś

E-Mail Website
Guest Editor
Institute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmonskiego Street, 51-630 Wrocław, Poland
Interests: modeling and optimization in agricultural engineering; electrical parameters of biological materials; bioinformatics; artificial intelligence; computational intelligence; pattern classification; clustering; artificial neural networks; food science; evolutionary algorithms; neural modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Tomasz Wojciechowski

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: agricultural engineering; soil tillage; precision agriculture; soil monitoring; proximal sensing; spectroscopy; digital farming; smart farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gniewko Niedbała

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial intelligence; machine learning; yield modeling; predictions; forecasting; crop production; artificial neural networks; predictive analytics and artificial intelligence in decision-making; applied computer science; information technologies; computer-aided decision support systems; wireless communication systems; computer models of empirical systems; precision agriculture; agrotechnologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the success of the first edition of this Special Issue of Applied Science, entitled “New Development in Smart Farming for Sustainable Agriculture”, we have launched a second edition.

Smart farming is a relatively new farming management concept marking the evolution from precision to digital agriculture, generating a fourth wave of the agricultural revolution (Agriculture 4.0). It combines precision agriculture with digital technology. Many techniques and tools, such as Artificial Intelligence, the Internet of Things, big data analysis, machine learning, modern communication technologies, GNSS and Earth observation systems, drones, robots and automation systems, are employed to make modern agriculture more “intelligent” and “smart”.

The smart farming approach is becoming an essential step in the development of sustainable agriculture, reducing the environmental impact of farming, making agriculture more profitable for farmers, and increasing consumer acceptance of agricultural technologies and products. Modern sensors collect information about the parameters of crops, soil, water, etc. The acquired data are stored, processed, and analyzed, and valuable knowledge is extracted. Control and decision support systems use knowledge for the optimization and automatization of agricultural processes. All together, they will represent a technical revolution bringing about major changes and agricultural practice. Such deep changes in practice bring not only opportunities but also big challenges.

This Special Issue aims to present state-of-the-art papers related to a wide range of reviews, research papers, communications, technical papers, research concepts, and perspectives in the applications and benefits of smart farming in sustainable agriculture.

Some of the topics of interest in this Special Issue include (but are not limited to):

  • Smart farming technologies for sustainable crop, animal, and fish production;
  • Sustainable, data-driven agri-food supply chain;
  • Remote sensing for sustainable smart farming modeling and optimization of agricultural processes;
  • Modeling and optimization of automation and robotization systems for sustainable farming;
  • Smart sensors and the Internet of Things for sustainable agriculture;
  • Decision support systems and data analysis in sustainable agriculture;
  • Artificial intelligence, machine learning, and deep learning application for sustainable agriculture;
  • Cloud computing and big data analysis in the sustainable agri-food sector.

Prof. Dr. Katarzyna Pentoś
Dr. Tomasz Wojciechowski
Prof. Dr. Gniewko Niedbała
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 250 words) can be sent to the Editorial Office for assessment.

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

  • smart farming
  • precision agriculture
  • precision livestock
  • precision horticulture
  • digital agriculture
  • remote sensing
  • internet of things
  • data analysis
  • sustainable agriculture
  • traceability
  • supply chain
  • artificial neural networks
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 6199 KB  
Article
High-Precision Peanut Pod Detection Device Based on Dual-Route Attention Mechanism
by Yongkuai Chen, Pengyan Chang, Tao Wang and Jian Zhao
Appl. Sci. 2026, 16(1), 418; https://doi.org/10.3390/app16010418 - 30 Dec 2025
Viewed by 462
Abstract
Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which [...] Read more.
Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which is inefficient and time-consuming. To improve detection efficiency and accuracy, this study proposes an improved BTM-YOLOv8 model and tests it on an independently designed pod detection device. In the backbone network, the BiFormer module is introduced, employing a dual-route attention mechanism with dynamic, content-aware, and query-adaptive sparse attention to extract features from densely packed peanuts. In addition, the Triple Attention mechanism is incorporated to strengthen the model’s multidimensional interaction and feature responsiveness. Finally, the original CIoU loss function is replaced with MPDIoU loss, simplifying distance metric computation and enabling more scale-focused optimization in bounding box regression. The results show that BTM-YOLOv8 has stronger detection performance for ‘Quan Hua 557’ peanut pods, with precision, recall, mAP50, and F1 score reaching 98.40%, 96.20%, 99.00%, and 97.29%, respectively. Compared to the original YOLOv8, these values improved by 3.9%, 2.4%, 1.2%, and 3.14%, respectively. Ablation experiments further validate the effectiveness of the introduced modules, showing reduced attention to irrelevant information, enhanced target feature capture, and lower false detection rates. Through comparisons with various mainstream deep learning models, it was further demonstrated that BTM-YOLOv8 performs well in detecting ‘Quan Hua 557’ peanut pods. When comparing the device’s detection results with manual counts, the R2 value was 0.999, and the RMSE value was 12.69, indicating high accuracy. This study improves the efficiency of ‘Quan Hua 557’ peanut pod detection, reduces labor costs, and provides quantifiable data support for breeding, offering a new technical reference for the detection of other crops. Full article
(This article belongs to the Special Issue New Developments in Smart Farming Applied in Sustainable Agriculture, 2nd Edition)
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29 pages, 10201 KB  
Article
Hybrid Methodological Evaluation Using UAV/Satellite Information for the Monitoring of Super-Intensive Olive Groves
by Esther Alfonso, Serafín López-Cuervo, Julián Aguirre, Enrique Pérez-Martín and Iñigo Molina
Appl. Sci. 2025, 15(20), 11171; https://doi.org/10.3390/app152011171 - 18 Oct 2025
Cited by 2 | Viewed by 1359
Abstract
Advances in Earth observation technology using multispectral imagery from satellite Earth observation systems and sensors mounted on unmanned aerial vehicles (UAVs) are enabling more accurate crop monitoring. These images, once processed, facilitate the analysis of crop health by enabling the study of crop [...] Read more.
Advances in Earth observation technology using multispectral imagery from satellite Earth observation systems and sensors mounted on unmanned aerial vehicles (UAVs) are enabling more accurate crop monitoring. These images, once processed, facilitate the analysis of crop health by enabling the study of crop vigour, the calculation of biomass indices, and the continuous temporal monitoring using vegetation indices (VIs). These indicators allow for the identification of diseases, pests, or water stress, among others. This study compares images acquired with the Altum PT sensor (UAV) and Super Dove (satellite) to evaluate their ability to detect specific problems in super-intensive olive groves at two critical times: January, during pruning, and April, at the beginning of fruit development. Four different VIs were used, and multispectral maps were generated for each: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), the Normalized Difference Red Edge Index (NDRE) and the Leaf Chlorophyll Index (LCI). Data for each plant (n = 11,104) were obtained for analysis across all dates and sensors. A combined methodology (Spearman’s correlation coefficient, Student’s t-test and decision trees) was used to validate the behaviour of the variables and propose predictive models. The results showed significant differences between the sensors, with a common trend in spatial patterns and a correlation range between 0.45 and 0.68. Integrating both technologies enables multiscale assessment, optimizing agronomic management and supporting more sustainable precision agriculture. Full article
(This article belongs to the Special Issue New Developments in Smart Farming Applied in Sustainable Agriculture, 2nd Edition)
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19 pages, 7604 KB  
Article
Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series
by Dorijan Radočaj, Ivan Plaščak and Mladen Jurišić
Appl. Sci. 2025, 15(13), 7216; https://doi.org/10.3390/app15137216 - 26 Jun 2025
Cited by 6 | Viewed by 2732
Abstract
Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap [...] Read more.
Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article
(This article belongs to the Special Issue New Developments in Smart Farming Applied in Sustainable Agriculture, 2nd Edition)
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