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Agronomy
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Machine Learning Applications in Digital Agriculture
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Machine Learning Applications in Digital Agriculture

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 111905

Special Issue Editors

Prof. Dr. Thomas Scholten

E-Mail Website
Guest 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. Ruhollah Taghizadeh-Mehrjardi

E-Mail Website
Guest 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
Dr. Karsten Schmidt

E-Mail Website
Guest 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

Special Issue Information

Dear Colleagues,

Machine learning—the scientific field that gives machines the ability to learn without being strictly programmed—can make agriculture more efficient and effective. An increasing amount of sophisticated data, from remote sensing and especially from proximal sensing, make 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 help to minimize negative consequences like soil erosion, soil compaction, and organic carbon and biodiversity loss. In the last few years, different machine learning techniques (e.g., artificial neural networks, decision tree, support vector machine, ensemble models, deep learning), different geophysical sensor platforms, as well as newly available satellite data have been tested and applied in precision agriculture. This Special Issue on Machine Learning Applications in Digital Agriculture provides international coverage of advances in the development and application of machine learning for solving problems in agriculture disciplines like 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. Ruhollah Taghizadeh-Mehrjardi
Dr. Karsten Schmidt
Guest Editor

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 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
  • deep learning
  • digital agriculture
  • precision farming

Published Papers (16 papers)

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15 pages, 1126 KiB  
Article
Rice Blast (Magnaporthe oryzae) Occurrence Prediction and the Key Factor Sensitivity Analysis by Machine Learning
by Li-Wei Liu, Sheng-Hsin Hsieh, Su-Ju Lin, Yu-Min Wang and Wen-Shin Lin
Agronomy 2021, 11(4), 771; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040771 - 15 Apr 2021
Cited by 19 | Viewed by 4301
Abstract
This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. [...] Read more.
This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. The developed multilayer perceptron (MLP), support vector machine (SVM), Elman recurrent neural network (Elman RNN) and probabilistic neural network (PNN) were evaluated by F-measures. Finally, a sensitivity analysis (SA) was conducted for the factor importance assessment. The study result shows that the PNN performed best with the F-measure (β = 2) of 96.8%. The SA was conducted in the PNN model resulting in the main effect period is 10 days before the rice blast happened. The key factors found are minimum air temperature, followed by solar energy and equaled sensitivity of average relative humidity, maximum air temperature and soil temperature. The temperature phase lag in air and soil may cause a lower dew point and suitable for rice blast pathogens growth. Through this study’s results, rice blast warnings can be issued 10 days in advance, increasing the response time for farmers preparing related preventive measures, further reducing the losses caused by rice blast. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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18 pages, 10156 KiB  
Article
AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment
by Pedro Faria, Telmo Nogueira, Ana Ferreira, Cristina Carlos and Luís Rosado
Agronomy 2021, 11(4), 731; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040731 - 10 Apr 2021
Cited by 10 | Viewed by 3018
Abstract
The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production [...] Read more.
The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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21 pages, 4220 KiB  
Article
Can We Use Machine Learning for Agricultural Land Suitability Assessment?
by Anders Bjørn Møller, Vera Leatitia Mulder, Gerard B. M. Heuvelink, Niels Mark Jacobsen and Mogens Humlekrog Greve
Agronomy 2021, 11(4), 703; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040703 - 07 Apr 2021
Cited by 20 | Viewed by 5799
Abstract
It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. [...] Read more.
It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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15 pages, 17811 KiB  
Article
Total and Hot-Water Extractable Organic Carbon and Nitrogen in Organic Soil Amendments: Their Prediction Using Portable Mid-Infrared Spectroscopy with Support Vector Machines
by Ralf Wehrle, Gerhard Welp and Stefan Pätzold
Agronomy 2021, 11(4), 659; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040659 - 30 Mar 2021
Cited by 8 | Viewed by 3664
Abstract
Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon [...] Read more.
Against the background of climate change mitigation, organic amendments (OA) may contribute to store carbon (C) in soils, given that the OA provide a sufficient stability and resistance to degradation. In terms of the evaluation of OA behavior in soil, total organic carbon (TOC), total nitrogen (TN), and the ratio of TOC to TN (CN-ratio) are important basic indicators. Hot-water extractable carbon (hwC) and nitrogen (hwN) as well as their ratios to TOC and TN are appropriate to characterize a labile pool of organic matter. As for quickly determining these properties, mid-infrared spectroscopy (MIRS) in combination with calibrations based on machine learning methods are potentially capable of analyzing various OA attributes. Recently available portable devices (pMIRS) might replace established benchtop devices (bMIRS) as they have potential for on-site measurements that would facilitate the workflow. Here, we used non-linear support vector machines (SVM) to calibrate prediction models for a heterogeneous dataset of greenwaste composts and biochar compost substrates (BCS) (n = 45) using bMIRS and pMIRS instruments on ground samples. Calibrated models for both devices were validated on separate test sets and showed similar results. Ten OA were sieved to particle size classes (psc’s) of >4 mm, 2–4 mm, 0.5–2 mm, and <0.5 mm. A universal SVM model was then developed for all OA and psc’s (n = 162) via pMIRS. Validation revealed that the models provided reliable predictions for most parameters (R2 = 0.49–0.93; ratio of performance to interquartile distance (RPIQ) = 1.19–5.70). We conclude that (i) the examined parameters are sensitive towards chemical composition of OA as well as particle size distribution and can therefore be used as indicators for labile carbon and nitrogen pools of OA, (ii) prediction models based on SVM and pMIRS are a feasible approach to predict the examined C and N pools in organic amendments and their particle size class, and (iii) pMIRS can provide valuable information for optimized application of OA on cultivated soils at low costs and efforts. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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20 pages, 8483 KiB  
Article
Land Use and Soil Organic Carbon Stocks—Change Detection over Time Using Digital Soil Assessment: A Case Study from Kamyaran Region, Iran (1988–2018)
by Kamal Nabiollahi, Shadi Shahlaee, Salahudin Zahedi, Ruhollah Taghizadeh-Mehrjardi, Ruth Kerry and Thomas Scholten
Agronomy 2021, 11(3), 597; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11030597 - 21 Mar 2021
Cited by 8 | Viewed by 3025
Abstract
Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 [...] Read more.
Land use change and soil organic carbon stock (SOCS) depletion over time is one of the predominant worldwide environmental problems related to global warming and the need to secure food production for an increasing world population. In our research, satellite images from 1988 and 2018 were analyzed for a 177.48 km2 region in Kurdistan Province, Iran. Across the study area. 186 disturbed and undisturbed soil samples were collected at two depths (0–20 cm and 20–50 cm). Bulk density (BD), soil organic carbon (SOC), rock fragments (RockF) and SOCS were measured. Random forest was used to model the spatial variability of SOCS. Land use was mapped with supervised classification and maximum likelihood approaches. The Kappa index and overall accuracy of the supervised classification and maximum likelihood land use maps varied between 83% and 88% and 78% and 85%, respectively. The area of forest and high-quality rangeland covered 5286 ha in 1988 and decreased by almost 30% by 2018. Most of the decrease was due to the establishment of cropland and orchards, and due to overgrazing of high-quality rangeland. As expected, the results of the analysis of variance showed that mean values of SOCS for the high-quality rangeland and forest were significantly higher compared to other land use classes. Thus, transformation of land with natural vegetation like forest and high-quality rangeland led to a loss of 15,494 Mg C in the topsoil, 15,475 Mg C in the subsoil and 15,489 Mg C−1 in total. We concluded that the predominant causes of natural vegetation degradation in the study area were mostly due to the increasing need for food, anthropogenic activities such as cultivation and over grazing, lack of government landuse legislation and the results of this study are useful for land use monitoring, decision making, natural vegetation planning and other areas of research and development in Kurdistan province. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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16 pages, 1202 KiB  
Article
Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada
by Abdelkarim Lajili, Athyna N. Cambouris, Karem Chokmani, Marc Duchemin, Isabelle Perron, Bernie J. Zebarth, Asim Biswas and Viacheslav I. Adamchuk
Agronomy 2021, 11(3), 432; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11030432 - 26 Feb 2021
Cited by 13 | Viewed by 3087
Abstract
Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification [...] Read more.
Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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15 pages, 1856 KiB  
Article
Soil Moisture Retrieval Model Design with Multispectral and Infrared Images from Unmanned Aerial Vehicles Using Convolutional Neural Network
by Min-Guk Seo, Hyo-Sang Shin and Antonios Tsourdos
Agronomy 2021, 11(2), 398; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11020398 - 23 Feb 2021
Cited by 6 | Viewed by 2635
Abstract
This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for high spatial resolutions of [...] Read more.
This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for high spatial resolutions of airborne images and easy operations. A combination of multispectral and infrared (IR) sensors is applied to overcome the effects of canopies convering the field on the sensor measurements. Convolutional neural network (CNN) is utilized to take the measurement images directly as inputs for the soil moisture retrieval model without loss of information. The procedures to obtain an input image corresponding to a certain soil moisture level measurement point are addressed, and the overall structure of the proposed CNN-based model is suggested with descriptions. Training and testing of the proposed soil moisture retrieval model are conducted to verify and validate its performance and address the effects of input image sizes and errors on input images. The soil moisture level estimation performance decreases when the input image size increases as the ratio of the pixel corresponding to the point to estimate soil moisture level to the total number of pixels in the input image, whereas the input image size should be large enough to include this pixel under the errors in input images. The comparative study shows that the proposed CNN-based algorithm is advantageous on estimation performance by maintaining spatial information of pixels on the input images. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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15 pages, 4152 KiB  
Article
The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach
by Samreen Naeem, Aqib Ali, Christophe Chesneau, Muhammad H. Tahir, Farrukh Jamal, Rehan Ahmad Khan Sherwani and Mahmood Ul Hassan
Agronomy 2021, 11(2), 263; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11020263 - 30 Jan 2021
Cited by 45 | Viewed by 39900
Abstract
This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) [...] Read more.
This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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21 pages, 3269 KiB  
Article
Predicting Net Returns of Organic and Conventional Strawberry Following Soil Disinfestation with Steam or Steam Plus Additives
by Aleksandr Michuda, Rachael E. Goodhue, Mark Hoffmann and Steven A. Fennimore
Agronomy 2021, 11(1), 149; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11010149 - 14 Jan 2021
Cited by 6 | Viewed by 2085
Abstract
Pre-plant methods for managing soil-borne pests and diseases are an important priority for many agricultural production systems. This study investigates whether the application of steam is an economically sustainable pre-plant soil disinfestation technique for organic and conventional strawberry (Fragaria ananassa) production [...] Read more.
Pre-plant methods for managing soil-borne pests and diseases are an important priority for many agricultural production systems. This study investigates whether the application of steam is an economically sustainable pre-plant soil disinfestation technique for organic and conventional strawberry (Fragaria ananassa) production in California’s Central Coast region. We analyze net returns from field trials using steam and steam + mustard seed meal (MSM) as pre-plant soil disinfestation treatments. ANOVA tests identify statistically significant differences in net revenues by treatment and trial. Multivariate regressions estimate the magnitude of these effects. Predictive polynomial models identify relationships between net returns and two treatment characteristics: maximum temperature (°C) and time at ≥60 °C (minutes). For organic production, net returns are statistically similar for the steam and steam + MSM treatments. For conventional production, the steam + MSM treatment has significantly higher net returns than the steam treatment. Cross-validated polynomial models outperform the sample mean for prediction of net returns, except for the steam + MSM treatment in conventional production. The optimal degree of the polynomial ranges from 1–4 degrees, depending on the production system and treatment. Results from two of three organic models suggest that maximum soil temperatures of 62–63 °C achieved for 41–44 min maximizes net returns and may be a basis for further experiments. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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20 pages, 1218 KiB  
Article
Influence of Soil Type on the Reliability of the Prediction Model for Bioavailability of Mn, Zn, Pb, Ni and Cu in the Soils of the Republic of Serbia
by Jelena Maksimović, Radmila Pivić, Aleksandra Stanojković-Sebić, Marina Jovković, Darko Jaramaz and Zoran Dinić
Agronomy 2021, 11(1), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11010141 - 13 Jan 2021
Cited by 7 | Viewed by 2505
Abstract
The principles of sustainable agriculture in the 21st century are based on the preservation of basic natural resources and environmental protection, which is achieved through a multidisciplinary approach in obtaining solutions and applying information technologies. Prediction models of bioavailability of trace elements (TEs) [...] Read more.
The principles of sustainable agriculture in the 21st century are based on the preservation of basic natural resources and environmental protection, which is achieved through a multidisciplinary approach in obtaining solutions and applying information technologies. Prediction models of bioavailability of trace elements (TEs) represent the basis for the development of machine learning and artificial intelligence in digital agriculture. Since the bioavailability of TEs is influenced by the physicochemical properties of the soil, which are characteristic of the soil type, in order to obtain more reliable prediction models in this study, the testing set from the previous study was grouped based on the soil type. The aim of this study was to examine the possibility of improvement in the prediction of bioavailability of TEs by using a different strategy of model development. After the training set was grouped based on the criteria for the new model development, the developed basic models were compared to the basic models from the previous study. The second step was to develop models based on the soil type (for the eight most common soil types in the Republic of Serbia—RS) and to compare their reliability to the basic models. From the total number of developed models by soil type (80), 75% were accepted as statistically reliable for predicting the bioavailability of TEs by soil type and 70% of prediction models had a higher determination coefficient (R2), compared to the basic models. For the Fluvisol soil type, all prediction models were accepted, while the least reliable prediction was for the Planosol type. As in the previous study of bioavailability prediction for TEs, the prediction models for Cu stood out, with more than half of the models with R2 greater than 0.90. Results of this study indicated that the formation of a testing set by soil type derives models whose predictions are more reliable than the basic ones. To improve the performance of prediction models, it is necessary to include additional physicochemical parameters and to conduct an adequate analysis of extensive testing sets with more comprehensive statistical techniques. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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19 pages, 4058 KiB  
Article
A Possible Role of Copernicus Sentinel-2 Data to Support Common Agricultural Policy Controls in Agriculture
by Filippo Sarvia, Elena Xausa, Samuele De Petris, Gianluca Cantamessa and Enrico Borgogno-Mondino
Agronomy 2021, 11(1), 110; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11010110 - 08 Jan 2021
Cited by 32 | Viewed by 3349
Abstract
Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology [...] Read more.
Farmers that intend to access Common Agricultural Policy (CAP) contributions must submit an application to the territorially competent Paying Agencies (PA). Agencies are called to verify consistency of CAP contributions requirements through ground campaigns. Recently, EU regulation (N. 746/2018) proposed an alternative methodology to control CAP applications based on Earth Observation data. Accordingly, this work was aimed at designing and implementing a prototype of service based on Copernicus Sentinel-2 (S2) data for the classification of soybean, corn, wheat, rice, and meadow crops. The approach relies on the classification of S2 NDVI time-series (TS) by “user-friendly” supervised classification algorithms: Minimum Distance (MD) and Random Forest (RF). The study area was located in the Vercelli province (NW Italy), which represents a strategic agricultural area in the Piemonte region. Crop classes separability proved to be a key factor during the classification process. Confusion matrices were generated with respect to ground checks (GCs); they showed a high Overall Accuracy (>80%) for both MD and RF approaches. With respect to MD and RF, a new raster layer was generated (hereinafter called Controls Map layer), mapping four levels of classification occurrences, useful for administrative procedures required by PA. The Control Map layer highlighted that only the eight percent of CAP 2019 applications appeared to be critical in terms of consistency between farmers’ declarations and classification results. Only for these ones, a GC was warmly suggested, while the 12% must be desirable and the 80% was not required. This information alone suggested that the proposed methodology is able to optimize GCs, making possible to focus ground checks on a limited number of fields, thus determining an economic saving for PA and/or a more effective strategy of controls. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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12 pages, 6314 KiB  
Article
Leaf Segmentation and Classification with a Complicated Background Using Deep Learning
by Kunlong Yang, Weizhen Zhong and Fengguo Li
Agronomy 2020, 10(11), 1721; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111721 - 06 Nov 2020
Cited by 68 | Viewed by 8335
Abstract
The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are [...] Read more.
The segmentation and classification of leaves in plant images are a great challenge, especially when several leaves are overlapping in images with a complicated background. In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied. First, more than 2500 leaf images with a complicated background are collected and artificially labeled with target pixels and background pixels. Two-thousand of them are fed into a Mask Region-based Convolutional Neural Network (Mask R-CNN) to train a model for leaf segmentation. Then, a training set that contains more than 1500 training images of 15 species is fed into a very deep convolutional network with 16 layers (VGG16) to train a model for leaf classification. The best hyperparameters for these methods are found by comparing a variety of parameter combinations. The results show that the average Misclassification Error (ME) of 80 test images using Mask R-CNN is 1.15%. The average accuracy value for the leaf classification of 150 test images using VGG16 is up to 91.5%. This indicates that these methods can be used to segment and classify the leaf image with a complicated background effectively. It could provide a reference for the phenotype analysis and automatic classification of plants. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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16 pages, 6290 KiB  
Article
Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application
by Yudhi Adhitya, Setya Widyawan Prakosa, Mario Köppen and Jenq-Shiou Leu
Agronomy 2020, 10(11), 1642; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111642 - 25 Oct 2020
Cited by 31 | Viewed by 5703
Abstract
The implementation of Industry 4.0 emphasizes the capability and competitiveness in agriculture application, which is the essential framework of a country’s economy that procures raw materials and resources. Human workers currently employ the traditional assessment method and classification of cocoa beans, which requires [...] Read more.
The implementation of Industry 4.0 emphasizes the capability and competitiveness in agriculture application, which is the essential framework of a country’s economy that procures raw materials and resources. Human workers currently employ the traditional assessment method and classification of cocoa beans, which requires a significant amount of time. Advanced agricultural development and procedural operations differ significantly from those of several decades earlier, principally because of technological developments, including sensors, devices, appliances, and information technology. Artificial intelligence, as one of the foremost techniques that revitalized the implementation of Industry 4.0, has extraordinary potential and prospective applications. This study demonstrated a methodology for textural feature analysis on digital images of cocoa beans. The co-occurrence matrix features of the gray level co-occurrence matrix (GLCM) were compared with the convolutional neural network (CNN) method for the feature extraction method. In addition, we applied several classifiers for conclusive assessment and classification to obtain an accuracy performance analysis. Our results showed that using the GLCM texture feature extraction can contribute more reliable results than using CNN feature extraction from the final classification. Our method was implemented through on-site preprocessing within a low-performance computational device. It also helped to foster the use of modern Internet of Things (IoT) technologies among farmers and to increase the security of the food supply chain as a whole. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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23 pages, 19617 KiB  
Article
Classification of Basal Stem Rot Disease in Oil Palm Plantations Using Terrestrial Laser Scanning Data and Machine Learning
by Nur A. Husin, Siti Khairunniza-Bejo, Ahmad F. Abdullah, Muhamad S. M. Kassim, Desa Ahmad and Mohd H. A. Aziz
Agronomy 2020, 10(11), 1624; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10111624 - 22 Oct 2020
Cited by 18 | Viewed by 5305
Abstract
The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown [...] Read more.
The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown size. The effect of this disease depends on the severity of the infection. Currently, the disease can be detected manually by analyzing the oil palm tree’s physical structure. Terrestrial laser scanning (TLS) is an active ranging method that uses laser light, which can directly represent the tree’s external structure. This study aimed to classify the healthiness levels of the BSR disease using a machine learning (ML) approach. A total of 80 oil palm trees with four different healthiness levels were pre-determined by the experts during data collection with 40 each for training and testing. The four healthiness levels are T0 (healthy), T1 (mildly infected), T2 (moderately infected), and T3 (severely infected), with 10 trees in each level. A terrestrial scanner was mounted at a height of 1 m, and each oil palm was scanned at four positions at a distance of 1.5 m around the tree. Five tree features were extracted from the TLS data: C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), frond angle, and frond number. C200 and C850 were obtained using the crown stratification method, while the other three features were obtained from the top-down image. The obtained features were then analyzed by principal component analysis (PCA) to reduce the dimensionality of the dataset and increase its interpretability while at the same time minimizing information loss. The results showed that the kernel naïve Bayes (KNB) model developed using the input parameters of the principal components (PCs) 1 and 2 had the best performance among 90 other models with a multiple level accuracy of 85% and a Kappa coefficient of 0.80. Furthermore, the combination of the two highest PC variance with the most weighted to frond number, frond angle, crown area, and C200 significantly contributed to the classification success. The model also could classify healthy and mildly infected trees with 100% accuracy. Therefore, it can be concluded that the ML approach using TLS data can be used to predict early BSR infection with high accuracy. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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16 pages, 17040 KiB  
Article
A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning
by Chunlong Zhang, Kunlin Zou and Yue Pan
Agronomy 2020, 10(7), 972; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy10070972 - 06 Jul 2020
Cited by 34 | Viewed by 4223
Abstract
Apples are one of the most kind of important fruit in the world. China has been the largest apple producing country. Yield estimating, robot harvesting, precise spraying are important processes for precise planting apples. Image segmentation is an important step in machine vision [...] Read more.
Apples are one of the most kind of important fruit in the world. China has been the largest apple producing country. Yield estimating, robot harvesting, precise spraying are important processes for precise planting apples. Image segmentation is an important step in machine vision systems for precision apple planting. In this paper, an apple fruit segmentation algorithm applied in the orchard was studied. The effect of many color features in classifying apple fruit pixels from other pixels was evaluated. Three color features were selected. This color features could effectively distinguish the apple fruit pixels from other pixels. The GLCM (Grey-Level Co-occurrence Matrix) was used to extract texture features. The best distance and orientation parameters for GLCM were found. Nine machine learning algorithms had been used to develop pixel classifiers. The classifier was trained with 100 pixels and tested with 100 pixels. The accuracy of the classifier based on Random Forest reached 0.94. One hundred images of an apple orchard were artificially labeled with apple fruit pixels and other pixels. At the same time, a classifier was used to segment these images. Regression analysis was performed on the results of artificial labeling and classifier classification. The average values of Af (segmentation error), FPR (false positive rate) and FNR (false negative rate) were 0.07, 0.13 and 0.15, respectively. This result showed that this algorithm could segment apple fruit in orchard images effectively. It could provide a reference for precise apple planting management. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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Review

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22 pages, 1659 KiB  
Review
Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review
by Bini Darwin, Pamela Dharmaraj, Shajin Prince, Daniela Elena Popescu and Duraisamy Jude Hemanth
Agronomy 2021, 11(4), 646; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040646 - 27 Mar 2021
Cited by 80 | Viewed by 12062
Abstract
Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in [...] Read more.
Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic. Full article
(This article belongs to the Special Issue Machine Learning Applications in Digital Agriculture)
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