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Applications of Machine Learning Technology in Agricultural Data Mining
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Applications of Machine Learning Technology in Agricultural Data Mining

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 January 2025 | Viewed by 3288

Special Issue Editors

Dr. Basarab Matei

E-Mail Website
Guest Editor
LIPN, Sorbonne Paris North University, Paris, France
Interests: machine learning; statistical learning; representation learning
Dr. Petru Alexandru Vlaicu

E-Mail Website
Guest Editor
National Research and Development Institute for Animal Biology and Nutrition, Balotesti, Romania
Interests: food quality; bioactive compounds; fatty acids; antioxidants; functional foods; feed; food; animals; nutrition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, there is an imperative need for machine learning technology integration within the agricultural sector to optimize processes, productivity, and resource allocation, as well as to analyse, quantify, monitor, and enhance the overall sustainability of agricultural practices. 

However, we can go further with the realization of machine learning's applications and data analysis capabilities, as it can be used to address modern agricultural challenges spanning cost forecasting, predictions pertaining to agricultural outputs, efficient livestock management, informed soil strategies, and precise production planning, in addition to extending to food product quality and insightful consumer analytics as well as labour reduction.

Amid global food security concerns and a heightened awareness of consumption patterns, this discourse places emphasis on employing machine learning techniques to enhance agricultural product quality for interpreting extensive datasets, revealing quantitative outcomes and intricate interrelationships among variables. Therefore, fostering the research and development of machine learning applications in agriculture becomes paramount, uniting researchers from diverse scientific disciplines to deliberate upon this shared interest.

This Special Issue focuses on the role of machine learning technologies in agricultural data mining, with the aim to share quality research concerning its applications in the diverse agriculture sector, including any type of crop production, livestock, storage, or food predictions. 

Dr. Basarab Matei
Dr. Petru Alexandru Vlaicu
Guest Editors

Manuscript Submission Information

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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

  • machine learning
  • data mining
  • crop management/prediction
  • quality assessment
  • storage conditions
  • product quality
  • food quality
  • predictions
  • livestock management/production
  • soil management
  • production planning
  • consumer analytics
  • smart agricultural industry

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Published Papers (4 papers)

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Research

17 pages, 1556 KiB  
Article
Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
by Tomislav Ivanovski, Marko Gulić and Maja Matetić
Appl. Sci. 2024, 14(18), 8539; https://doi.org/10.3390/app14188539 - 23 Sep 2024
Viewed by 427
Abstract
The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste [...] Read more.
The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method. Full article
(This article belongs to the Special Issue Applications of Machine Learning Technology in Agricultural Data Mining)
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17 pages, 1702 KiB  
Article
Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks
by Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis C. Tagarakis, Dimitrios Kateris and Dionysis Bochtis
Appl. Sci. 2024, 14(18), 8520; https://doi.org/10.3390/app14188520 - 21 Sep 2024
Viewed by 585
Abstract
This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing [...] Read more.
This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. Full article
(This article belongs to the Special Issue Applications of Machine Learning Technology in Agricultural Data Mining)
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26 pages, 6811 KiB  
Article
Fd-CasBGRel: A Joint Entity–Relationship Extraction Model for Aquatic Disease Domains
by Hongbao Ye, Lijian Lv, Chengquan Zhou and Dawei Sun
Appl. Sci. 2024, 14(14), 6147; https://doi.org/10.3390/app14146147 - 15 Jul 2024
Viewed by 640
Abstract
Entity–relationship extraction plays a pivotal role in the construction of domain knowledge graphs. For the aquatic disease domain, however, this relationship extraction is a formidable task because of overlapping relationships, data specialization, limited feature fusion, and imbalanced data samples, which significantly weaken the [...] Read more.
Entity–relationship extraction plays a pivotal role in the construction of domain knowledge graphs. For the aquatic disease domain, however, this relationship extraction is a formidable task because of overlapping relationships, data specialization, limited feature fusion, and imbalanced data samples, which significantly weaken the extraction’s performance. To tackle these challenges, this study leverages published books and aquatic disease websites as data sources to compile a text corpus, establish datasets, and then propose the Fd-CasBGRel model specifically tailored to the aquatic disease domain. The model uses the Casrel cascading binary tagging framework to address relationship overlap; utilizes task fine-tuning for better performance on aquatic disease data; trains on specialized aquatic disease corpora to improve adaptability; and integrates the BRC feature fusion module—which incorporates self-attention mechanisms, BiLSTM, relative position encoding, and conditional layer normalization—to leverage entity position and context for enhanced fusion. Further, it replaces the traditional cross-entropy loss function with the GHM loss function to mitigate category imbalance issues. The experimental results indicate that the F1 score of the Fd-CasBGRel on the aquatic disease dataset reached 84.71%, significantly outperforming several benchmark models. This model effectively addresses the challenges of ternary extraction’s low performance caused by high data specialization, insufficient feature integration, and data imbalances. The model achieved the highest F1 score of 86.52% on the overlapping relationship category dataset, demonstrating its robust capability in extracting overlapping data. Furthermore, We also conducted comparative experiments on the publicly available dataset WebNLG, and the model in this paper obtained the best performance metrics compared to the rest of the comparative models, indicating that the model has good generalization ability. Full article
(This article belongs to the Special Issue Applications of Machine Learning Technology in Agricultural Data Mining)
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17 pages, 2283 KiB  
Article
Research on Outgoing Moisture Content Prediction Models of Corn Drying Process Based on Sensitive Variables
by Simin Xing, Zimu Lin, Xianglan Gao, Dehua Wang, Guohui Liu, Yi Cao and Yadi Liu
Appl. Sci. 2024, 14(13), 5680; https://doi.org/10.3390/app14135680 - 28 Jun 2024
Cited by 1 | Viewed by 608
Abstract
Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study [...] Read more.
Accurate prediction of outgoing moisture content is the key to achieving energy-saving and efficient technological transformation of drying. This study relies on a grain drying simulation experiment system which combined counter and current drying sections to design corn kernel drying experiments. This study obtains 18 kinds of temperature and humidity variables during the drying process and uses Uninformative Variable Elimination (UVE) method to screen sensitive variables affecting the outgoing moisture content. Subsequently, six prediction models for the outgoing corn moisture content were developed, innovatively incorporating Multiple Linear Regression (MLR), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM). The results show that eight sensitive variables have been screened to predict the moisture content of outgoing corn. The sensitive variables effectively reduced the redundancy and multicollinearity of data in the MLR model and improved the coefficient of determination (R2) of ELM and LSTM models by 0.02 and 0.05. The MLR prediction model established based on the full set of temperature and humidity data has an R2 of 0.910 and a root-mean-square error (RMSE) of 0.881%, while the UVE-ELM and UVE-LSTM prediction models achieve a better fitting effect and prediction accuracy. The UVE-LSTM model is set with a batch size of 30, a learning rate of 0.01, and 100 iterations. For the training set of UVE-LSTM, the R2 value is 0.931 and the RMSE value is 0.711%. The UVE-ELM model, with sigmoid as the activation function and 14 neurons configured, runs fast and has the best prediction accuracy. The R2 values of UVE-ELM training set and validation set are 0.943 and 0.946, respectively, and the RMSEs are 0.544% and 0.581%. The models proposed in this study provide data reference and technical support for process optimization and automation control of the corn drying process. Full article
(This article belongs to the Special Issue Applications of Machine Learning Technology in Agricultural Data Mining)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Application of predictive analytics for waste reduction in the food sector
Authors: Pedro C. Santana-Mancilla1; Raymundo Buenrostro-Mariscal1; Irma L. Galván-Espinoza1; César J. Ramírez-Manzo1; Luis E. Anido-Rifón2, *
Affiliation: 1 School of Telematics, Universidad de Colima, Colima 28040, Mexico 2 atlanTTic Research Center, School of Telecommunications Engineering, University of Vigo, 36310 Vigo, Spain
Abstract: Food waste is a global issue with significant economic, environmental, and ethical implications. The integration of machine learning (ML) and predictive analytics presents an opportunity to tackle this challenge effectively. This article proposes to examine the use of ML algorithms in forecasting food demand and spoilage, thus enabling more efficient food consumption and distribution. By analyzing patterns in consumer behavior, supply chain bottlenecks, and perishability of products, ML can inform smarter decision-making that reduces food waste.

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