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New Development in Smart Farming for Sustainable Agriculture

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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 April 2024) | Viewed by 23452

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Special Issue Editors

Prof. Dr. Katarzyna Pentoś

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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

Prof. Dr. Gniewko Niedbała

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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
Special Issues, Collections and Topics in MDPI journals

Dr. Tomasz Wojciechowski

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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: agricutural engineering; soil tillage; precison agriculture; soil monitoring; proximal sensing; spectroscopy; digital farming; smart farming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart farming is a relatively new farming management concept evolving 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, development of 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 toward sustainable agriculture by 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 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
Prof. Dr. Gniewko Niedbała
Dr. Tomasz Wojciechowski
Guest Editors

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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

Published Papers (13 papers)

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Research

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15 pages, 2724 KiB

Open AccessArticle

The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming

by Vidas Žuraulis and Robertas Pečeliūnas

Appl. Sci. 2023, 13(20), 11216; https://doi.org/10.3390/app132011216 - 12 Oct 2023

Cited by 1 | Viewed by 1180

Abstract

Monitoring and control systems integrated into agricultural machinery enable the development of agricultural analyses with advanced management tools, but the full use of all available data is often limited by the lack of uniformity among data transmitted from different agricultural machines. This paper presents an agricultural data aggregation and conversion model that allows for the collection and use of data captured from different agricultural machines in the course of work; these data differ in their original file formats and cannot be combined and used in a common analysis system. Programming work was carried out to create the model, and a specialised software interface enabled raster data processing using a Python library together with the open-source Hypertext Preprocessor and JavaScript programming language libraries. A PostGIS extension was utilised to engage field geometry and map-layering tools. Model validation showed that the data aggregation and conversion functions ensure the evaluation of semantic content and the transformation of the aggregated data into a unified format which is suitable for further use in intelligent farming management applications. The developed model will encourage precision agriculture, with the aim of improving work efficiency and the rational use of resources, the economy, and ecology in agriculture. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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19 pages, 2599 KiB

Open AccessArticle

A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images

by Fathimathul Rajeena P.P., Walaa N. Ismail and Mona A. S. Ali

Appl. Sci. 2023, 13(12), 7083; https://doi.org/10.3390/app13127083 - 13 Jun 2023

Cited by 7 | Viewed by 1331

Abstract

There are several major threats to crop production. As herbicide use has become overly reliant on weed control, herbicide-resistant weeds have evolved and pose an increasing threat to the environment, food safety, and human health. Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of images for the identification of weeds from crop images that are captured by drones. Manually designing such neural architectures is, however, an error-prone and time-consuming process. Natural-inspired optimization algorithms have been widely used to design and optimize neural networks, since they can perform a blackbox optimization process without explicitly formulating mathematical formulations or providing gradient information to develop appropriate representations and search paradigms for solutions. Harris Hawk Optimization algorithms (HHO) have been developed in recent years to identify optimal or near-optimal solutions to difficult problems automatically, thus overcoming the limitations of human judgment. A new automated architecture based on DenseNet-121 and DenseNet-201 models is presented in this study, which is called “DenseHHO”. A novel CNN architecture design is devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters. Based on the results of this study, the proposed method is capable of detecting weeds in unstructured field environments with an average accuracy of 98.44% using DenseNet-121 and 97.91% using DenseNet-201, the highest accuracy among optimization-based weed-detection strategies. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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20 pages, 2606 KiB

Open AccessArticle

Agricultural Social Networks: An Agricultural Value Chain-Based Digitalization Framework for an Inclusive Digital Economy

by Ronald Tombe and Hanlie Smuts

Appl. Sci. 2023, 13(11), 6382; https://doi.org/10.3390/app13116382 - 23 May 2023

Cited by 3 | Viewed by 3373

Abstract

Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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13 pages, 2070 KiB

Open AccessArticle

Eco-Efficiency in Mushroom Production: A Study on HVAC Equipment to Reduce Energy Consumption and CO₂ Emissions

by Alexandre F. Santos, Pedro D. Gaspar and Heraldo J. L. de Souza

Appl. Sci. 2023, 13(10), 6129; https://doi.org/10.3390/app13106129 - 17 May 2023

Cited by 1 | Viewed by 1346

Abstract

The mushroom market has seen accelerated growth in today’s world. Despite advances in technology, harvesting is a more artisanal procedure. Countries such as Portugal and Brazil are not self-sufficient in mushroom production. Among the difficulties in the production of mushrooms is the question of acclimatization using temperature and relative humidity control. An experimental study was conducted. Energy analyzers were placed in the lighting, acclimatization, and water pumping system to produce 2200 kg of mushrooms in an acclimatized shed with an area of 100 m². Energy consumptions of 48 kWh for lighting, 1575 kWh for air conditioning, and 9 kWh for pumping water were determined. A TEWI index of 0.7515 kWh/kg of Paris-type mushroom (Agaricus bisporus) was found. With equipment using R-454 B as a refrigerant, the estimated TEWI using the proposed HVAC equipment model was 0.537 kWh/kg, and CO₂ emissions were reduced from 18,219 to 5324.81, a reduction of 70%. Thus, the proposed HVAC equipment model can potentially decrease greenhouse gas emissions and energy consumption in mushroom production, making a step towards achieving sustainability and mitigating climate change. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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11 pages, 1185 KiB

Open AccessArticle

Matching the Liquid Atomization Model to Experimental Data Obtained from Selected Nozzles

by Beata Cieniawska, Stanisław Parafiniuk, Paweł A. Kluza and Zdzisław Otachel

Appl. Sci. 2023, 13(7), 4433; https://doi.org/10.3390/app13074433 - 31 Mar 2023

Cited by 1 | Viewed by 1018

Abstract

The spraying procedure is one of the most difficult operations in agricultural production. Achieving the desired effectiveness of the procedure is dependent on obtaining an appropriate level and uniformity of liquid distribution. The aim of this paper was to present a liquid decomposition model generated on the basis of experimental data. The tests were carried out on a test stand, which consisted of a container with nozzles and a grooved table. The experiments were carried out with the use of selected standard, anti-drift, and air-induction single-stream nozzles at constant liquid pressure. The optimization process was carried out in Microsoft Excel Solver. Furthermore, in order to compare the data generated by the model with the data from the virtual boom, we applied an analysis of correlation and linear regression in the Statistica 13.1 software. Based on the results obtained, it can be concluded that the model is a good fit to the experimental data (R² > 0.95). The model, which was generated on the basis of experimental data, will facilitate control of the operation and degree of wear of nozzles, which will contribute to ensuring uniform spraying. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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26 pages, 17621 KiB

Open AccessArticle

Nano Aerial Vehicles for Tree Pollination

by Isabel Pinheiro, André Aguiar, André Figueiredo, Tatiana Pinho, António Valente and Filipe Santos

Appl. Sci. 2023, 13(7), 4265; https://doi.org/10.3390/app13074265 - 28 Mar 2023

Cited by 2 | Viewed by 1964

Abstract

Currently, Unmanned Aerial Vehicles (UAVs) are considered in the development of various applications in agriculture, which has led to the expansion of the agricultural UAV market. However, Nano Aerial Vehicles (NAVs) are still underutilised in agriculture. NAVs are characterised by a maximum wing length of 15 centimetres and a weight of fewer than 50 g. Due to their physical characteristics, NAVs have the advantage of being able to approach and perform tasks with more precision than conventional UAVs, making them suitable for precision agriculture. This work aims to contribute to an open-source solution known as Nano Aerial Bee (NAB) to enable further research and development on the use of NAVs in an agricultural context. The purpose of NAB is to mimic and assist bees in the context of pollination. We designed this open-source solution by taking into account the existing state-of-the-art solution and the requirements of pollination activities. This paper presents the relevant background and work carried out in this area by analysing papers on the topic of NAVs. The development of this prototype is rather complex given the interactions between the different hardware components and the need to achieve autonomous flight capable of pollination. We adequately describe and discuss these challenges in this work. Besides the open-source NAB solution, we train three different versions of YOLO (YOLOv5, YOLOv7, and YOLOR) on an original dataset (Flower Detection Dataset) containing 206 images of a group of eight flowers and a public dataset (TensorFlow Flower Dataset), which must be annotated (TensorFlow Flower Detection Dataset). The results of the models trained on the Flower Detection Dataset are shown to be satisfactory, with YOLOv7 and YOLOR achieving the best performance, with 98% precision, 99% recall, and 98% F1 score. The performance of these models is evaluated using the TensorFlow Flower Detection Dataset to test their robustness. The three YOLO models are also trained on the TensorFlow Flower Detection Dataset to better understand the results. In this case, YOLOR is shown to obtain the most promising results, with 84% precision, 80% recall, and 82% F1 score. The results obtained using the Flower Detection Dataset are used for NAB guidance for the detection of the relative position in an image, which defines the NAB execute command. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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21 pages, 11151 KiB

Open AccessFeature PaperArticle

Evaluation of a Deep Learning Approach for Predicting the Fraction of Transpirable Soil Water in Vineyards

by Khadijeh Alibabaei, Pedro D. Gaspar, Rebeca M. Campos, Gonçalo C. Rodrigues and Carlos M. Lopes

Appl. Sci. 2023, 13(5), 2815; https://doi.org/10.3390/app13052815 - 22 Feb 2023

Viewed by 1195

Abstract

As agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an

R^{2}

-score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an

R^{2}

-score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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17 pages, 2343 KiB

Open AccessArticle

Evaluation of Multiple Linear Regression and Machine Learning Approaches to Predict Soil Compaction and Shear Stress Based on Electrical Parameters

by Katarzyna Pentoś, Jasper Tembeck Mbah, Krzysztof Pieczarka, Gniewko Niedbała and Tomasz Wojciechowski

Appl. Sci. 2022, 12(17), 8791; https://doi.org/10.3390/app12178791 - 01 Sep 2022

Cited by 16 | Viewed by 2727

Abstract

This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0–0.5 m and 0.4–0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R < 0.5) were produced by the multiple linear regression. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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15 pages, 5740 KiB

Open AccessArticle

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

by Rick van Essen, Ben Harel, Gert Kootstra and Yael Edan

Appl. Sci. 2022, 12(9), 4414; https://doi.org/10.3390/app12094414 - 27 Apr 2022

Cited by 2 | Viewed by 1752

Abstract

This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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21 pages, 5111 KiB

Open AccessArticle

Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing

by Julyanne Braga Cruz Amaral, Fernando Bezerra Lopes, Ana Caroline Messias de Magalhães, Sebastian Kujawa, Carlos Alberto Kenji Taniguchi, Adunias dos Santos Teixeira, Claudivan Feitosa de Lacerda, Thales Rafael Guimarães Queiroz, Eunice Maia de Andrade, Isabel Cristina da Silva Araújo and Gniewko Niedbała

Appl. Sci. 2022, 12(1), 458; https://doi.org/10.3390/app12010458 - 04 Jan 2022

Cited by 5 | Viewed by 2012

Abstract

Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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10 pages, 3313 KiB

Open AccessArticle

How to Harvest Haylage Bales in Sustainable Agriculture

by Sylwester Borowski, Jerzy Kaszkowiak and Edmund Dulcet

Appl. Sci. 2021, 11(23), 11508; https://doi.org/10.3390/app112311508 - 04 Dec 2021

Cited by 3 | Viewed by 1897

Abstract

Storing silage in round bales (balage) is a commonly used method for preserving forage for use as stock fodder that has a higher nutritional content than hay. Baling at the optimum density is important for ensuring ideal fermentation conditions. In the manuscript, we present the research methodology and the results of the experiment. We did experiments over the density of haylage bales. We investigated the effect of the moisture content in the harvested material, the length of the cut material and the pressing pressure in the round baler. We used the Barenbrug BG-5 forage mix at different moisture content levels (69, 63, 56, 49, and 42%), that was either unchopped or chopped by the round baler’s cutter bars (312 and 183 mm length) and baled at three different pressing pressures (0.9, 1.4, and 1.8 MPa). The results showed that forage density in the bales reached the highest value at a moisture content of 56% and a pressing pressure of 1.8 MPa, with the forage chopped by the cutter bars. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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Review

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14 pages, 831 KiB

Open AccessReview

Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation

by Sara M. Marcelino, Pedro Dinis Gaspar, Arminda do Paço, Tânia M. Lima, Ana Monteiro, José Carlos Franco, Erika S. Santos, Rebeca Campos and Carlos M. Lopes

Appl. Sci. 2024, 14(6), 2622; https://doi.org/10.3390/app14062622 - 21 Mar 2024

Viewed by 483

Abstract

Agriculture intensification has driven the loss of biodiversity at a global level. The implementation of strategies to conserve and promote biodiversity in agricultural areas can be favoured by adequate assessment methods that foster the awareness of decision makers about the impact of management practices. This paper presents a state-of-the-art review of assessment methods of the overall biodiversity in agricultural systems, focusing on the quantitative methods applied, indicators of biodiversity, and functionalities. It was concluded that compensation effects and difficulties in interpretation are associated with currently common methodologies of composite indicator calculation to assess biodiversity performance. This review allowed for the identification and critical analysis of current methodologies for biodiversity assessments in the agricultural sector, and it highlighted the need for more implementation-oriented approaches. By providing recommendations on what should be considered when formulating biodiversity assessment methods, this study can contribute to the formulation of appropriate assessment frameworks for agricultural management policies and strategies. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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Other

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11 pages, 2187 KiB

Open AccessTechnical Note

Profitability Assessment of Precision Agriculture Applications—A Step Forward in Farm Management

by Christos Karydas, Myrto Chatziantoniou, Ourania Tremma, Alexandros Milios, Kostas Stamkopoulos, Vangelis Vassiliadis and Spiros Mourelatos

Appl. Sci. 2023, 13(17), 9640; https://doi.org/10.3390/app13179640 - 25 Aug 2023

Viewed by 808

Abstract

Profitability is not given the necessary attention in contemporary precision agriculture. In this work, a new tool, namely ProFit, is developed within a pre-existing farm management system, namely ifarma, to assess the profitability of precision agriculture applications in extended crops, as most of the current solutions available on the market respond inadequately to this need. ProFit offers an easy-to-use interface to enter financial records, while it uses the dynamic map view environment of ifarma to display the profitability maps. Worked examples reveal that profitability maps end up being quite different from yield maps in site-specific applications. The module is regulated at a 5 m spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of ifarma, together with a flexible architecture allowing future interventions and improvements, renders ProFit an innovative commercial tool. Full article

(This article belongs to the Special Issue New Development in Smart Farming for Sustainable Agriculture)

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