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Engineering of Smart Agriculture—2nd Edition
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Engineering of Smart 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: 16 December 2024 | Viewed by 8970

Special Issue Editors

Dr. Paweł Kiełbasa

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Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116, Kraków, Poland
Interests: machine management in agriculture; ergonomics in agricultural technology; electromagnetic identification of plant quality structure; soil type; subsoil compaction; agricultural engineering; electromagnetism
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Miernik

E-Mail Website
Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Kraków, Balicka 116B, 30-149 Krakow, Poland
Interests: agricultural microbiology; agrobiotechnology; precision agriculture; nanotechnology; electromagnetic effects on microorganisms and ergonomics
Dr. Akinniyi Akinsunmade

E-Mail Website
Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116, Kraków, Poland
Interests: earth and environmental sciences; geophysical methods; ground penetrating radar (GPR); electrical and electromagnetic; magnetic and gravity; seismic refraction and reflections; soil properties assessments; precision agriculture

Special Issue Information

Dear Colleagues,

Modern agricultural production has two main tasks that now must coexist. The first is yield maximization in order to satisfy market needs, and the second is minimization of the interference with the soil environment. One of the basic criteria of a balance between these tasks is the degree of soil biological improvement, the parameterization of which is an important issue in modern production systems. Among the innovative technologies that have been developed in the last few decades, precision agriculture can be considered the most important; it is considered to be an excellent tool for the development of sustainable agriculture and allows us to optimize production for present and future generations while taking into account economic, ecological, and social aspects. This concept was born from the conviction that the variability in plant growth conditions is the factor that contributes most to the variability in yields at the field scale and, therefore, it would be advantageous to adapt the amount of input to the local soil conditions and to perform the right treatment in the right place at the right time. A very important issue is the search for the most effective methods that will allow us to delineate in the field areas differing in production conditions, among which soil properties are the most important. A number of technologically advanced devices have been developed, thanks to which large amounts of data can be acquired in real time under field conditions in a continuous measurement mode using proximity detection. Modern technical solutions allow for the integration of satellite-based surface condition identification systems with ground-based systems and aircraft. Integrating various measurements into a single system for mapping soil properties is a current research problem. It is predicted that geophysical surveys with the simultaneous use of more sensors will become the standard because of the broad range of field information necessary for proper management. Modern farm and production technologies are monitored through the use of telematic systems and software that allow for real-time analysis and then simulation of the economic outcome of a given activity or process, which consequently leads to its optimization. In addition, networking of the entire machine park enables us to automatically plan maintenance services.

Dr. Paweł Kiełbasa
Dr. Anna Miernik
Dr. Akinniyi Akinsunmade
Guest Editors

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Keywords

  • precision agriculture
  • telematics
  • geoinformatics
  • agricultural production technology
  • measurement systems
  • agricultural engineering
  • mechanical engineering
  • geophysics
  • soil
  • plant

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Published Papers (9 papers)

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Research

17 pages, 3920 KiB  
Article
A Novel Methodological Approach to Simulating the Growth of Photosynthetic Organisms Using Long-Term Meteorological Sequences: A Case Study of Microalgae (Chlorella vulgaris)
by Ousmane Wane, Luis F. Zarzalejo, Francisco Ferrera-Cobos, Ana A. Navarro and Rita X. Valenzuela
Appl. Sci. 2024, 14(22), 10580; https://doi.org/10.3390/app142210580 - 16 Nov 2024
Viewed by 685
Abstract
The growth of photosynthetic organisms requires specific ranges of temperature and photosynthetically active radiation. Monitoring and maintaining these conditions is technically difficult, especially in outdoor cultures. In such cases, a typical meteorological sequence can be a useful tool for estimating the growth of [...] Read more.
The growth of photosynthetic organisms requires specific ranges of temperature and photosynthetically active radiation. Monitoring and maintaining these conditions is technically difficult, especially in outdoor cultures. In such cases, a typical meteorological sequence can be a useful tool for estimating the growth of photosynthetic organisms. This study proposes a new methodology based on long-term meteorological sequences to simulate the growth of photosynthetic organisms. This case study addresses microalgae growth simulation (Chlorella vulgaris) in Riosequillo in the north of the Madrid region (Spain) for the four seasons of the year. Then, these estimates are compared with the observed results of an experimental culture of microalgae in domestic wastewater. The results also show strong agreement with the probability distribution function of the daily biomass concentration, giving the best results for typical summer and spring meteorological sequences. The methodology seems to confirm the representativeness of typical meteorological sequences, allows for the identification of the most likely production scenarios for project feasibility analyses, and may be applied to decision-making processes. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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16 pages, 3477 KiB  
Article
Design and Performance Evaluation of an In Situ Online Soil Electrical Conductivity Sensor Prototype Based on the High-Performance Integrated Chip AD5941
by Runze Song and Man Zhang
Appl. Sci. 2024, 14(17), 7788; https://doi.org/10.3390/app14177788 - 3 Sep 2024
Viewed by 642
Abstract
Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection [...] Read more.
Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection principle, in this study, we aim to improve the limitations of the constant current source, selecting the high-performance integrated chip AD5941, optimizing the detection circuit and probe structure, improving the achievability of the detection circuit, and designing a type of in situ on-line real-time access to a soil electrical conductivity detection device, and improve the detection accuracy by temperature compensation. In this paper, dynamic performance, steady state performance, radial sensitivity range, and calibration test are carried out for the soil electrical conductivity detection prototype. The test results show that the dynamic response speed of the prototype is less than 50 ms, the steady state error is not more than ±2%, and the radial measurement sensitivity range is 8~10 cm. A comparison with the commercial sensor shows that the linear fit of the two measurements reaches 0.9995, and the absolute error ranges from −61.40 µS/cm to 23.90 µS/cm, with a relative error range of −1.94~1.86%. It shows that the performance of the two sensors is comparable, but the quality/price ratio of the prototype is much higher than that of the commercialized product. In this study, it is demonstrated that a high-precision, low-cost, and easy-to-use in situ online soil electrical conductivity detection device can be provided for agricultural and forestry production. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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13 pages, 7713 KiB  
Article
Assessment of Soil Horizons and Their Matric Potential from Ground-Penetrating Radar Signal Attributes
by Akinniyi Akinsunmade, Paweł Pysz, Mirosław Zagórda, Anna Miernik and Sylwia Tomecka-Suchoń
Appl. Sci. 2024, 14(16), 7328; https://doi.org/10.3390/app14167328 - 20 Aug 2024
Viewed by 599
Abstract
Soil plays significant roles in different phases and in the continuous existence of human life. Its comprehensive knowledge, particularly as related to its physical characteristics, enhances its utilization, conservation, and management. The traditional methods of soil study are characterized with some pitfalls such [...] Read more.
Soil plays significant roles in different phases and in the continuous existence of human life. Its comprehensive knowledge, particularly as related to its physical characteristics, enhances its utilization, conservation, and management. The traditional methods of soil study are characterized with some pitfalls such as much time needed to perform such assessments. There are also issues of invasiveness that affect the soil structures and discrete sampling that may not reflect true spatial attributes in the outcome of such techniques. These problems are largely due to the concealing nature of soil layers that made its thorough evaluation difficult. In this study, an alternative geophysical approach has been adopted. The technique is the ground-penetrating method (GPR) that utilizes electromagnetic pulse energy via its equipment’s sensors, which can allow the investigation of soil properties, even in its concealing state. This study aimed at qualitatively evaluating the soil horizons and the matric potentials using the GPR signal attributes within the unsaturated zone with a view of having insight into the test field’s characterization. Field data measurements were obtained using MALA ProEX GPR equipment with its accessories manufactured by MALA Geosciences, Stockholm, Sweden. Evaluation of the processed field data results and computed attributes show soil characteristics variations with depth that was interpreted as the layers. This can be seen from the GPR data presentation as an image representing the subsurface of the zones of propagation of the pulse energy. Spectral analysis of the GPR signals allows for the delineation of two zones of contrasting features, which were tagged as high and low matric potentials. Although the conventional direct measurement of the matric potential was not made at the time of the study to complement and confirm the veracity of the approach, the results indicate the possibility of the approach towards a quick and in situ technique of soil investigations. Such evaluation may be valuable input in precision agriculture where accurate data are sought for implementation. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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14 pages, 2903 KiB  
Article
Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2
by Nengtian Peng, Ming Chen and Guofu Feng
Appl. Sci. 2024, 14(12), 4982; https://doi.org/10.3390/app14124982 - 7 Jun 2024
Viewed by 823
Abstract
The Chinese mitten crab (Eriocheir sinensis), a species unique to Chinese aquaculture, holds significant economic value in the seafood market. In response to increasing concerns about the quality and safety of Chinese mitten crab products, the high traceability costs, and challenges [...] Read more.
The Chinese mitten crab (Eriocheir sinensis), a species unique to Chinese aquaculture, holds significant economic value in the seafood market. In response to increasing concerns about the quality and safety of Chinese mitten crab products, the high traceability costs, and challenges for consumers in verifying the authenticity of individual crabs, this study proposes a lightweight individual recognition model for Chinese mitten crab carapace images based on an improved MobileNetV2. The method first utilizes a lightweight backbone network, MobileNetV2, combined with a coordinate attention mechanism to extract features of the Chinese mitten crab carapace, thereby enhancing the ability to recognize critical morphological features of the crab shell while maintaining the model’s light weight. Then, the model is trained using the ArcFace loss function, which effectively extracts the generalized features of the Chinese mitten crab carapace images. Finally, authenticity is verified by calculating the similarity between two input images of Chinese mitten crab carapaces. Experimental results show that the model, combined with the coordinate attention mechanism and ArcFace, achieves a high accuracy rate of 98.56% on the Chinese mitten crab image dataset, surpassing ShuffleFaceNet, MobileFaceNet, and VarGFaceNet by 13.63, 11.1, and 6.55 percentage points, respectively. Moreover, it only requires an average of 1.7 milliseconds per image for verification. While maintaining lightness, this model offers high efficiency and accuracy, offering an effective technical solution for enhancing the traceability of Chinese mitten crab products and combating counterfeit goods. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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15 pages, 2255 KiB  
Article
A Preprocessing Technique Using Diffuse Reflectance Spectroscopy to Predict the Soil Properties of Paddy Fields in Korea
by Juwon Shin, Dae-Cheol Kim, Yongjin Cho, Myongkyoon Yang and Woo-Jae Cho
Appl. Sci. 2024, 14(11), 4673; https://doi.org/10.3390/app14114673 - 29 May 2024
Viewed by 786
Abstract
In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the [...] Read more.
In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky–Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R2 = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R2 = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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16 pages, 7318 KiB  
Article
Non-Contact Tilapia Mass Estimation Method Based on Underwater Binocular Vision
by Guofu Feng, Bo Pan and Ming Chen
Appl. Sci. 2024, 14(10), 4009; https://doi.org/10.3390/app14104009 - 8 May 2024
Cited by 1 | Viewed by 824
Abstract
The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation [...] Read more.
The non-destructive measurement of fish is an important link in intelligent aquaculture, and realizing the accurate estimation of fish mass is the key to the stable operation of this link. Taking tilapia as the object, this study proposes an underwater tilapia mass estimation method, which can accurately estimate the mass of free-swimming tilapia under non-contact conditions. First, image enhancement is performed on the original image, and the depth image is obtained by correcting and stereo matching the enhanced image using binocular stereo vision technology. And the fish body is segmented by an SAM model. Then, the segmented fish body is labeled with key points, thus realizing the 3D reconstruction of tilapia. Five mass estimation models are established based on the relationship between the body length and the mass of tilapia, so as to realize the mass estimation of tilapia. The results showed that the average relative errors of the method models were 5.34%~7.25%. The coefficient of determination of the final tilapia mass estimation with manual measurement was 0.99, and the average relative error was 5.90%. The improvement over existing deep learning methods is about 1.54%. This study will provide key technical support for the non-destructive measurement of tilapia, which is of great significance to the information management of aquaculture, the assessment of fish growth condition, and baiting control. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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13 pages, 1147 KiB  
Article
Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
by Wenxuan Jin, Mingjun Zheng and Pingzeng Liu
Appl. Sci. 2024, 14(9), 3809; https://doi.org/10.3390/app14093809 - 29 Apr 2024
Cited by 1 | Viewed by 827
Abstract
In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient [...] Read more.
In the specific application scenario of pepper product supply chain traceability, with the advancement of pepper product production, the expansion of links, and the increase of nodes, the quantity of data will become more and more enormous. The single-chain model is less efficient for querying if the data are all stored into the same blockchain. In order to improve the efficiency of blockchain data querying, this paper proposes a traceability model with one main chain and multiple side chain structures, which separate the uplinked data from each link and use multi-chain transactions to improve the efficiency of data queries. This model builds an indexing mechanism with a product traceability code, using one main chain and multiple side chains. The main and side chains form a one-to-many mapping relationship, storing the mapping relationship between the traceability code and the transaction address of the side chain traceability information in the main chain. This enables information to travel through the main chain traversal query based on the mapping relationship and then query the direct index out of the side chain, to achieve fast traceability query and improve the efficiency of querying. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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19 pages, 7716 KiB  
Article
The Use of a Double Bottom Trawl Set to Assess the Selectivity of Innovative Codends in Baltic Cod (Gadus morhua) Fishing
by Paweł Zalewski, Piotr Nowakowski, Krzysztof Berest, Grzegorz Krzemień, Jarosław Artyszuk, Artur Żuliński and Marta Kasprowicz
Appl. Sci. 2024, 14(3), 1131; https://doi.org/10.3390/app14031131 - 29 Jan 2024
Viewed by 1212
Abstract
The overall objective of the study presented in this paper was to evaluate the selectivity properties of three innovative codends in Baltic cod fishing: (1) an ultracross codend of 120 mm square mesh, (2) an ultracross codend with additional devices reducing the speed [...] Read more.
The overall objective of the study presented in this paper was to evaluate the selectivity properties of three innovative codends in Baltic cod fishing: (1) an ultracross codend of 120 mm square mesh, (2) an ultracross codend with additional devices reducing the speed of water flow—one tarpaulin diffuser and one net confusor of 120 mm mesh, and (3) an ultracross codend with two tarpaulin diffusers and two net confusors of 120 mm mesh. These codends were firstly tested at sea by single trawls and compared to T90 and herring codend trawls, allowing assessment of caught fish mass and dimensions. Additionally, a special divided small-mesh coat for the innovative codends enabled determination of the amount as well as the length and mass of cod escaping from the codend both while the trawling gear was towed and while it was hauled. Further validation of codend selectivity was carried out by a double (twin) bottom trawl set deployed from one cutter in various variants of innovative codends compared to standard ones. The results of the study indicate that the use of an innovative ultracross codend and innovative devices reducing the water flow speed (tarpaulin diffusers and 120 mm net confusors) significantly reduces the number of undersized cod (<35 cm), even down to 1.3%, in the haul and contributes to a reduction in invisible mortal discard. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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17 pages, 5111 KiB  
Article
Modelling Specific Energy Requirement for a Power-Operated Vertical Axis Rotor Type Intra-Row Weeding Tool Using Artificial Neural Network
by Satya Prakash Kumar, V. K. Tewari, Abhilash Kumar Chandel, C. R. Mehta, C. M. Pareek, C. R. Chethan and Brajesh Nare
Appl. Sci. 2023, 13(18), 10084; https://doi.org/10.3390/app131810084 - 7 Sep 2023
Cited by 5 | Viewed by 1637
Abstract
Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed [...] Read more.
Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed to estimate specific energy requirement of a vertical axis rotor type intra-row weeding tool. The depth of operation in soil bed, soil cone index, and forward/implement speed ratio (u/v) were selected as the input variables. Soil bin investigations were conducted using the vertical axis rotor (RVA), interfaced with draft, torque, speed sensors, and data acquisition system to record dynamic forces employed during soil–tool interaction at ranges of different operating parameters. The depth of operation (DO) had the maximum influence on the specific energy requirement of the RVA, followed by the cone index (CI) and the u/v ratio. The developed ANN model was able to predict the specific energy requirements of RVA at high accuracies as indicated by high R2 (0.91), low RMSE (0.0197) and low MAE (0.0479). Findings highlight the potential of the ANN as an efficient technique for modeling soil–tool interactions under specific experimental conditions. Such estimations will eventually optimize and enhance the performance efficiency of agricultural implements in the field. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture—2nd Edition)
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