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Agronomy
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Machine Learning in Digital Agriculture
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Machine Learning in Digital Agriculture

A topical collection in Agronomy (ISSN 2073-4395). This collection belongs to the section "Precision and Digital Agriculture".

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Editors

Prof. Dr. Thomas Scholten

E-Mail Website
Collection Editor
Eberhard Karls University Tübingen, Soil Science and Geomorphology, Rümelinstraße 19-23, D-72070 Tübingen, Germany
Interests: soil science; environment; geomorphology; geoecology; soil erosion; machine learning in soil science
Special Issues, Collections and Topics in MDPI journals
Dr. Karsten Schmidt

E-Mail Website
Collection Editor
eScience Center, University of Tübingen, Keplerstr. 2, 72076 Tübingen, Germany
Interests: digital soil mapping; precision farming; predictive modelling; representative soil sampling; geoinformatics; spatial statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Ruhollah Taghizadeh-Mehrjardi

E-Mail Website
Collection Editor
Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany
Interests: digital soil mapping; machine learning; pedology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues, 

Agriculture plays an important role in sustaining all human activities. The rapid increase in the world's population will further exacerbate food, water, and energy challenges. Digital agriculture—with precision farming, data analytics, machine learning, and artificial intelligence—has the potential to address the challenges of sustainable agricultural use. Machine learning—the scientific field that gives machines the ability to learn without being strictly programmed—has the potential to make agriculture more efficient and effective. The increasing amount of sophisticated data (e.g., remote sensing and proximal sensing) makes it possible to bridge the gap between data and decisions within agricultural planning. On-demand representative sampling and modeling of useful soil information in an unprecedented resolution leads to an improvement in the decision-making processes of, for example, liming, irrigation, fertilization, higher productivity, reduced waste in food, and biofuel production. Additionally, sustainable land management practices are only as good as the data they are made of, and they help to minimize negative consequences such as soil erosion, soil compaction, and organic carbon and biodiversity loss. In the last few years, different machine learning techniques, different geophysical sensor platforms, as well as newly available satellite data have been tested and applied in precision agriculture. This Collection on Machine Learning in Digital Agriculture provides international coverage of advances in the development and application of machine learning for solving problems in agricultural disciplines such as soil and water management. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers on topics pertaining to advances in the use of machine learning in agriculture are particularly welcomed.

Prof. Dr. Thomas Scholten
Dr. Karsten Schmidt
Dr. Ruhollah Taghizadeh-Mehrjardi
Collection 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 collection 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. Agronomy is an international peer-reviewed open access monthly 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 2600 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
  • artificial Intelligence
  • deep learning
  • cloud computing
  • modeling and predicting
  • digital agriculture
  • precision farming
  • digital agriculture
  • smart agriculture
  • agriculture intelligence
  • Internet of-Things (IoT)-based technologies
  • yield prediction
  • unmanned aerial systems (UAVs)
  • proximal sensing
  • remote sensing
  • GIS applications
  • image segmentation
  • multi/hyperspectral image analysis
  • computer vision
  • pattern recognition
  • big data analytics
  • laser scanner

Published Papers (6 papers)

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2024

Jump to: 2023, 2022, 2021

24 pages, 3898 KiB  
Review
An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower
by Luana Centorame, Thomas Gasperini, Alessio Ilari, Andrea Del Gatto and Ester Foppa Pedretti
Agronomy 2024, 14(4), 719; https://doi.org/10.3390/agronomy14040719 - 29 Mar 2024
Viewed by 558
Abstract
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied [...] Read more.
Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data. Full article
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2023

Jump to: 2024, 2022, 2021

20 pages, 3617 KiB  
Article
Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka
by Mathadadoddi Nanjundegowda Thimmegowda, Melekote Hanumanthaiah Manjunatha, Lingaraj Huggi, Huchahanumegowdanapalya Sanjeevaiah Shivaramu, Dadireddihalli Venkatappa Soumya, Lingegowda Nagesha and Hejjaji Sreekanthamurthy Padmashri
Agronomy 2023, 13(3), 704; https://doi.org/10.3390/agronomy13030704 - 27 Feb 2023
Cited by 3 | Viewed by 2066
Abstract
Two multivariate models were compared to assess their yield predictability based on long-term (1980–2021) rice yield and weather datasets over eleven districts of Karnataka. Simple multiple linear regression (SMLR) and artificial neural network models (ANN) were calibrated (1980–2019 data) and validated (2019–2020 data), [...] Read more.
Two multivariate models were compared to assess their yield predictability based on long-term (1980–2021) rice yield and weather datasets over eleven districts of Karnataka. Simple multiple linear regression (SMLR) and artificial neural network models (ANN) were calibrated (1980–2019 data) and validated (2019–2020 data), and yields were forecasted (2021). An intercomparison of the models revealed better yield predictability with ANN, as the observed deviations were smaller (−37.1 to 21.3%, 4% mean deviation) compared to SMLR (−2.5 to 35.0%, 16% mean deviation). Further, district-wise yield forecasting using ANN indicated an underestimation of yield, with higher errors in Mysuru (−0.2%), Uttara Kannada (−1.5%), Hassan (−0.1%), Ballari (−1.5%), and Belagavi (−15.3%) and overestimations in the remaining districts (0.0 to 4.2%) in 2018. Likewise, in 2019 the yields were underestimated in Kodagu (−0.6%), Shivamogga (−0.1%), Davanagere (−0.7%), Hassan (−0.2%), Ballari (−5.1%), and Belagavi (−10.8%) and overestimated for the other five districts (0.0 to 4.8%). Such model yield underestimations are related to the farmers’ yield improvement practices carried out under adverse weather conditions, which were not considered by the model while forecasting. As the deviations are in an acceptable range, they prove the better applicability of ANN for yield forecasting and crop management planning in addition to its use for regional agricultural policy making. Full article
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19 pages, 30030 KiB  
Article
Delineation of Soil Management Zone Maps at the Regional Scale Using Machine Learning
by Sedigheh Maleki, Alireza Karimi, Amin Mousavi, Ruth Kerry and Ruhollah Taghizadeh-Mehrjardi
Agronomy 2023, 13(2), 445; https://doi.org/10.3390/agronomy13020445 - 02 Feb 2023
Cited by 3 | Viewed by 2053
Abstract
Applying fertilizers to soil in a site-specific way that maximizes yields and minimizes environmental damage is an important goal. Developing soil management zones (MZs) is a suitable method for achieving sustainable agricultural production. Thus, this work aims to investigate MZs delineated based on [...] Read more.
Applying fertilizers to soil in a site-specific way that maximizes yields and minimizes environmental damage is an important goal. Developing soil management zones (MZs) is a suitable method for achieving sustainable agricultural production. Thus, this work aims to investigate MZs delineated based on the different soil properties using machine learning methods. To achieve these, 202 soil samples were collected at the agricultural land of pomegranate, pistachio, and saffron. A “random forest” model was applied to map soil properties based on environmental covariates. The predicted “Lin’s concordance correlation coefficient” values in validation soil properties varied from 0.65 to 0.79. The maps indicated low amounts of soil organic carbon, available potassium, available phosphate, and total nitrogen in most of the region. Furthermore, the study identified four different MZs according to relationships between soil properties and environmental covariates. Generally, the ranking of zones in terms of soil fertility was MZ4 > MZ1 > MZ3 > MZ2 based on the investigated soil properties and the soil quality (SQ) map. The five grades of SQ (i.e., very high, high, moderate, low, and very low) indicated that there was heterogeneous SQ in each MZ in the study area. There were 1.65 ha identified in MZ4 with very low SQ. This result is important in determining the amount of fertilizer to add to the soil in the different areas. It confirms the need for more specific regional management of agriculture lands in this region. Full article
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2022

Jump to: 2024, 2023, 2021

16 pages, 2914 KiB  
Article
Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset
by Sina Mallah, Bahareh Delsouz Khaki, Naser Davatgar, Thomas Scholten, Alireza Amirian-Chakan, Mostafa Emadi, Ruth Kerry, Amir Hosein Mosavi and Ruhollah Taghizadeh-Mehrjardi
Agronomy 2022, 12(11), 2613; https://doi.org/10.3390/agronomy12112613 - 24 Oct 2022
Cited by 6 | Viewed by 2279
Abstract
Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. While [...] Read more.
Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. While real-world datasets are inherently imbalanced, ML models overestimate the majority classes and underestimate the minority ones. The aim of this study was to investigate the effects of imbalance in training data on the performance of a random forest model (RF). The original dataset (imbalanced) included 6100 soil texture data from the surface layer of agricultural fields in northern Iran. A synthetic resampling approach using the synthetic minority oversampling technique (SMOTE) was employed to make a balanced dataset from the original data. Bioclimatic and remotely sensed data, distance, and terrain attributes were used as environmental covariates to model and map soil textural classes. Results showed that based on mean minimal depth (MMD), when imbalanced data was used, distance and annual mean precipitation were important, but when balanced data were employed, terrain attributes and remotely sensed data played a key role in predicting soil texture. Balanced data also improved the accuracies from 44% to 59% and 0.30 to 0.52 with regard to the overall accuracy and kappa values, respectively. Similar increasing trends were observed for the recall and F-scores. It is concluded that, in modeling soil texture classes using RF models through a digital soil mapping approach, data should be balanced before modeling. Full article
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18 pages, 66638 KiB  
Article
Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis
by Qi Wang, Danyao Jiang, Yifan Gao, Zijuan Zhang and Qingrui Chang
Agronomy 2022, 12(7), 1697; https://doi.org/10.3390/agronomy12071697 - 18 Jul 2022
Cited by 4 | Viewed by 2118
Abstract
A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of [...] Read more.
A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of the classical Geographically Weighted regression (GWR), Multi-scale Geographically Weighted regression (MGWR), and GWPCA-GWR. Our results revealed that local multi-collinearity on model fitting negatively affects the results to different degrees. Additionally, compared to other models, GWPCA-MGWR provided the lowest MAE (0.001) and little-to-no residual spatial autocorrelation and is the best model for regression for SOM spatial distribution and identification of dominant driving factors. GWPCA-MGWR produced spatial non-stationary SOM that was variably affected by soil nutrient content, soil type, and human activity, and was geomorphic in the second place. In conclusion, the spatial information obtained from GWPCA-MGWR provides a valuable reference for understanding the factors that influence SOM variation. Full article
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2021

Jump to: 2024, 2023, 2022

21 pages, 10332 KiB  
Article
FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards
by Faris A. Kateb, Muhammad Mostafa Monowar, Md. Abdul Hamid, Abu Quwsar Ohi and Muhammad Firoz Mridha
Agronomy 2021, 11(12), 2440; https://doi.org/10.3390/agronomy11122440 - 29 Nov 2021
Cited by 15 | Viewed by 3444
Abstract
Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible [...] Read more.
Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detecting fruits. The FruitDet architecture is designed on the YOLO pipeline and achieves better performance in detecting fruits than any other detection model. The backbone of the detection model is implemented using DenseNet architecture. Further, the FruitDet is packed with newer concepts: attentive pooling, bottleneck spatial pyramid pooling, and blackout mechanism. The detection mechanism is benchmarked using five datasets, which combines a total of eight different fruit classes. The FruitDet architecture acquires better performance than any other recognized detection methods in fruit detection. Full article
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