Applications of Data Analysis in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (25 September 2023) | Viewed by 32499

Special Issue Editor


E-Mail Website
Guest Editor
Department of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, Postal Box 275, 15424 Thessaloniki, Greece
Interests: artificial intelligence; biosystems engineering; automation; yield prediction; crop disease detection; weed management; remote sensing; data fusion; machine learning; deep learning; hyperspectral imaging; fluorescence kinetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The motivation behind this Agriculture Special Issue is to utilize “Data Analysis for Agriculture” in identifying key challenges that are faced by data analysts in attempting to solve problems faced by agriculture communities, to find potential solutions, and to identify the synergies needed between agriculture experts and data analytics experts. Therefore, we expect to obtain, from the domain experts, explanations on how they can apply data analytics, machine learning techniques, and sensor technologies in their scientific research via high impact publications in this Special Issue. We believe that this Special Issue will be a unique opportunity to highlight one of the main themes of next-generation digital agriculture, enabling agricultural decision making using data analytics and machine learning.

More specifically, the topics of interest include but are not limited to the following:

  • All aspects of data analytics technologies;
  • Architectures for data analytics systems;
  • Environmental data integration;
  • Smart farming and its application in data analysis;
  • Environmental data analysis;
  • Decision support systems for plant diseases;
  • Environmental data analysis and knowledge management;
  • Problems with data analysis in agriculture;
  • Machine learning in agricultural data analysis;
  • Deep learning in agricultural data analysis;
  • Artificial Intelligence in agricultural data analysis;
  • Remote and proximal sensing;
  • Multisensor data fusion.

Dr. Xanthoula Eirini Pantazi
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. Agriculture 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

  • data analytics
  • digital agriculture
  • agricultural decision support system
  • precision agriculture
  • machine learning in agriculture
  • deep learning in agriculture
  • artificial intelligence in agriculture
  • remote sensing in agriculture
  • proximal sensing in agriculture
  • multisensor data fusion

Published Papers (17 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 4442 KiB  
Article
A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop
by Konstantinos Dolaptsis, Xanthoula Eirini Pantazi, Charalampos Paraskevas, Selçuk Arslan, Yücel Tekin, Bere Benjamin Bantchina, Yahya Ulusoy, Kemal Sulhi Gündoğdu, Muhammad Qaswar, Danyal Bustan and Abdul Mounem Mouazen
Agriculture 2024, 14(2), 210; https://doi.org/10.3390/agriculture14020210 - 28 Jan 2024
Viewed by 753
Abstract
Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation [...] Read more.
Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R2 values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

19 pages, 27666 KiB  
Article
Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks
by Young-Jae La, Dasom Seo, Junhyeok Kang, Minwoo Kim, Tae-Woong Yoo and Il-Seok Oh
Agriculture 2023, 13(11), 2097; https://doi.org/10.3390/agriculture13112097 - 04 Nov 2023
Viewed by 1420
Abstract
Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, [...] Read more.
Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, our survey on tree segmentation revealed that no study has explicitly addressed this intertwining situation. This paper presents a novel dataset in which a precise tree region is labeled carefully by a human annotator by delineating the branches and trunk of a target apple tree. Because traditional rule-based image segmentation methods neglect semantic considerations, we employed cutting-edge deep learning models. Five recently pre-trained deep learning models for segmentation were modified to suit tree segmentation and were fine-tuned using our dataset. The experimental results show that YOLOv8 produces the best average precision (AP), 93.7 box AP@0.5:0.95 and 84.2 mask AP@0.5:0.95. We believe that our model can be successfully applied to various agricultural tasks. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

21 pages, 4806 KiB  
Article
SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction
by Ga-Ae Ryu, Tserenpurev Chuluunsaikhan, Aziz Nasridinov, HyungChul Rah and Kwan-Hee Yoo
Agriculture 2023, 13(11), 2044; https://doi.org/10.3390/agriculture13112044 - 24 Oct 2023
Viewed by 938
Abstract
In the domain of agricultural product sales and consumption forecasting, the presence of infrequent yet impactful events such as livestock epidemics and mass media influences poses substantial challenges. These rare occurrences, termed Sparse Critical Events (SCEs), often lead to predictions converging towards average [...] Read more.
In the domain of agricultural product sales and consumption forecasting, the presence of infrequent yet impactful events such as livestock epidemics and mass media influences poses substantial challenges. These rare occurrences, termed Sparse Critical Events (SCEs), often lead to predictions converging towards average values due to their omission from input candidate vectors. To address this issue, we introduce a modified Long Short-Term Memory (LSTM) model designed to selectively attend to and memorize critical events, emulating the human memory’s ability to retain crucial information. In contrast to the conventional LSTM model, which struggles with learning sparse critical event sequences due to its handling of forget gates and input vectors within the cell state, our proposed approach identifies and learns from sparse critical event sequences during data training. This proposed method, referred to as sparse critical event-driven LSTM (SCE-LSTM), is applied to predict purchase quantities of agricultural and livestock products using sharp-changing agricultural time-series data. For these predictions, we collected structured and unstructured data spanning the years 2010 to 2017 and developed the SCE-LSTM prediction model. Our model forecasts monetary expenditures for pork purchases over a one-month horizon. Notably, our results demonstrate that SCE-LSTM provides the closest predictions to actual daily pork purchase expenditures and exhibits the lowest error rates when compared to other prediction models. SCE-LSTM emerges as a promising solution to enhance agricultural product sales and consumption forecasts, particularly in the presence of rare critical events. Its superior performance and accuracy, as evidenced by our findings, underscore its potential significance in this domain. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

17 pages, 3735 KiB  
Article
Exploring the Potential Use of Sentinel-1 and 2 Satellite Imagery for Monitoring Winter Wheat Growth under Agricultural Drought Conditions in North-Western Poland
by Anna Jędrejek and Rafał Pudełko
Agriculture 2023, 13(9), 1798; https://doi.org/10.3390/agriculture13091798 - 12 Sep 2023
Cited by 1 | Viewed by 1040
Abstract
This paper presents analyses of the potential use of Sentinel-1 (S-1) and Sentinel-2 (S-2) imagery to generate models of winter wheat growth under agricultural drought vs. normal conditions identified based on potential yield losses calculated in the Agricultural Drought Monitoring System (ADMS). The [...] Read more.
This paper presents analyses of the potential use of Sentinel-1 (S-1) and Sentinel-2 (S-2) imagery to generate models of winter wheat growth under agricultural drought vs. normal conditions identified based on potential yield losses calculated in the Agricultural Drought Monitoring System (ADMS). The analyses carried out showed the sensitivity of satellite images to agricultural drought conditions determined in ADMS. The study was conducted in a large region, the West Pomeranian Voivodeship (NUTS PL42), and the analysis covered about 22,935 polygons with winter wheat production that constituted a total area of about 108,000 ha in the period from the 1st of April to the 1st of July 2021. For S-1 data, VH and VV backscatter and the VH/VV ratio were calculated, and for S-2 data, NDVI and NDWI indices were calculated, which were used to build models of winter wheat growth under water stress and in normal conditions. The obtained results presented in this work include: (i) Development of a test version of a model describing the winter wheat crop’s growth, with a preliminary assessment showing the potential for recognizing water shortage effects; and (ii) identification of promising indicators of water scarcity for crops, calculated based on S-1 and S-2 images, that could be recommended for application in remote sensing (RS) of drought effects as complementary multispectral and radar observations. The results obtained in this work also gave many clues regarding the direction and method of including satellite remote sensing in national monitoring programmes, which involves operations on many types of big data sets. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

23 pages, 5427 KiB  
Article
Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM
by Xintao Yuan, Xiao Zhang, Nannan Zhang, Rui Ma, Daidi He, Hao Bao and Wujun Sun
Agriculture 2023, 13(9), 1779; https://doi.org/10.3390/agriculture13091779 - 07 Sep 2023
Viewed by 1207
Abstract
Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the [...] Read more.
Rapid and non-destructive estimation of the chlorophyll content in cotton leaves is of great significance for the real-time monitoring of cotton growth under verticillium wilt (VW) stress. The spectral reflectance of healthy and VW cotton leaves was determined using hyperspectral technology, and the original spectra were processed using Savitzky–Golay (SG) smoothing, and on its basis through mean centering, standard normal variate (SG-SNV), multiplicative scatter correction (SG-MSC), reciprocal second-order differentiation, and logarithmic second-order differentiation ([lg(SG)]″) preprocessing operations. The characteristic bands were selected based on the correlation coefficient, vegetation index, successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS). The single-factor model, back propagation neural network of particle swarm optimization algorithm, and extreme learning machine (ELM) of a grey wolf optimizer (GWO) algorithm were constructed to compare and explore the ability of each model to estimate the soil plant analysis development (SPAD) value of cotton under VW stress. The results showed that spectral pretreatment could improve the correlation between characteristic bands and SPAD values. SG-MSC and SG-SNV showed better changes in the five pretreatments, and the maximum correlation coefficients of healthy and VW cotton leaves were higher than 0.74. Compared with SPA, the accuracy of model estimation based on CARS-extracted characteristic bands was higher, and the estimation accuracy of the multi-factor model was better than that of the single-factor model under each pretreatment. For healthy cotton leaves, [lg(SG)]″–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.956 and 0.887, respectively. For VW cotton leaves, SG-MSC–CARS–GWO–ELM was the optimal model, with a modeling and validation set R2 of 0.832 and 0.824, respectively. Therefore, the GWO–ELM model constructed under different pretreatments combined with characteristic extraction methods can be used for the estimation of leaf SPAD values under VW stress to dynamically monitor VW stress in cotton and provide a theoretical reference for precision agriculture. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

16 pages, 4371 KiB  
Article
Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture
by Tymoteusz Miller, Grzegorz Mikiciuk, Anna Kisiel, Małgorzata Mikiciuk, Dominika Paliwoda, Lidia Sas-Paszt, Danuta Cembrowska-Lech, Adrianna Krzemińska, Agnieszka Kozioł and Adam Brysiewicz
Agriculture 2023, 13(8), 1622; https://doi.org/10.3390/agriculture13081622 - 17 Aug 2023
Cited by 3 | Viewed by 2111
Abstract
Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, [...] Read more.
Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

22 pages, 39803 KiB  
Article
Instance Segmentation of Lotus Pods and Stalks in Unstructured Planting Environment Based on Improved YOLOv5
by Ange Lu, Lingzhi Ma, Hao Cui, Jun Liu and Qiucheng Ma
Agriculture 2023, 13(8), 1568; https://doi.org/10.3390/agriculture13081568 - 06 Aug 2023
Cited by 4 | Viewed by 1572
Abstract
Accurate segmentation of lotus pods and stalks with pose variability is a prerequisite for realizing the robotic harvesting of lotus pods. However, the complex growth environment of lotus pods causes great difficulties in conducting the above task. In this study, an instance segmentation [...] Read more.
Accurate segmentation of lotus pods and stalks with pose variability is a prerequisite for realizing the robotic harvesting of lotus pods. However, the complex growth environment of lotus pods causes great difficulties in conducting the above task. In this study, an instance segmentation model, LPSS-YOLOv5, for lotus pods and stalks based on the latest YOLOv5 v7.0 instance segmentation model was proposed. The CBAM attention mechanism was integrated into the network to improve the model’s feature extraction ability. The scale distribution of the multi-scale feature layer was adjusted, a 160 × 160 small-scale detection layer was added, and the original 20 × 20 large-scale detection layer was removed, which improved the model’s segmentation accuracy for small-scale lotus stalks and reduced the model size. On the medium-large scale test set, LPSS-YOLOv5 achieved a mask mAP0.5 of 99.3% for all classes. On the small-scale test set, the mAP0.5 for all classes and AP0.5 for stalks were 88.8% and 83.3%, which were 2.6% and 5.0% higher than the baseline, respectively. Compared with the mainstream Mask R-CNN and YOLACT models, LPSS-YOLOv5 showed a much higher segmentation accuracy, speed, and smaller size. The 2D and 3D localization tests verified that LPSS-YOLOv5 could effectively support the picking point localization and the pod–stalk affiliation confirmation. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

26 pages, 9049 KiB  
Article
Early Detection of Cavitation in Centrifugal Pumps Using Low-Cost Vibration and Sound Sensors
by Marios Karagiovanidis, Xanthoula Eirini Pantazi, Dimitrios Papamichail and Vassilios Fragos
Agriculture 2023, 13(8), 1544; https://doi.org/10.3390/agriculture13081544 - 02 Aug 2023
Cited by 3 | Viewed by 1107
Abstract
The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was [...] Read more.
The scope of this study is the evaluation of early detection methods for cavitation phenomena in centrifugal irrigation pumps by analyzing the produced vibration and sound signals from a low-cost sensor and data acquisition system and comparing several computational methods. Vibration data was acquired using the embedded accelerometer sensor of a smartphone device. Sound signals were obtained using the embedded microphone of the same commercial smartphone. The analysis was based on comparing the signals in different operating conditions with reference to the best efficiency operating point of the pump. In the case of vibrations, data was acquired for all three directional axes. The signals were processed by computational methods to extract the relative features in the frequency domain and use them to train an artificial neural network to be able to identify the different pump operating conditions while the cavitation phenomenon evolves. Three different classification algorithms were used to examine the most preferable approach for classifying data, namely the Classification Tree, the K-Nearest Neighbor, and the Support Vector Data algorithms. In addition, a convolutional neural network was utilized to examine the success rate of the classification when the datasets were formed as spectrograms instead. A detailed comparison of the classification algorithms and different axes was conducted. Comparing the results of the different methods for vibration and sound datasets, classification accuracy showed that in the case of vibration, the detection of cavitation in real conditions is possible, while it proves more challenging to identify cavitation conditions using sound data obtained with low-cost commercial sensors. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

17 pages, 4799 KiB  
Article
Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study
by Yonis Gulzar, Zeynep Ünal, Hakan Aktaş and Mohammad Shuaib Mir
Agriculture 2023, 13(8), 1479; https://doi.org/10.3390/agriculture13081479 - 26 Jul 2023
Cited by 27 | Viewed by 1895
Abstract
Sunflower is an important crop that is susceptible to various diseases, which can significantly impact crop yield and quality. Early and accurate detection of these diseases is crucial for implementing appropriate management strategies. In recent years, deep learning techniques have shown promising results [...] Read more.
Sunflower is an important crop that is susceptible to various diseases, which can significantly impact crop yield and quality. Early and accurate detection of these diseases is crucial for implementing appropriate management strategies. In recent years, deep learning techniques have shown promising results in the field of disease classification using image data. This study presents a comparative analysis of different deep-learning models for the classification of sunflower diseases. five widely used deep learning models, namely AlexNet, VGG16, InceptionV3, MobileNetV3, and EfficientNet were trained and evaluated using a dataset of sunflower disease images. The performance of each model was measured in terms of precision, recall, F1-score, and accuracy. The experimental results demonstrated that all the deep learning models achieved high precision, recall, F1-score, and accuracy values for sunflower disease classification. Among the models, EfficientNetB3 exhibited the highest precision, recall, F1-score, and accuracy of 0.979. whereas the other models, ALexNet, VGG16, InceptionV3 and MobileNetV3 achieved 0.865, 0.965, 0.954 and 0.969 accuracy respectively. Based on the comparative analysis, it can be concluded that deep learning models are effective for the classification of sunflower diseases. The results highlight the potential of deep learning in early disease detection and classification, which can assist farmers and agronomists in implementing timely disease management strategies. Furthermore, the findings suggest that models like MobileNetV3 and EfficientNetB3 could be preferred choices due to their high performance and relatively fewer training epochs. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

14 pages, 1928 KiB  
Article
Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation
by Alfadhl Y. Khaled, Nader Ekramirad, Kevin D. Donohue, Raul T. Villanueva and Akinbode A. Adedeji
Agriculture 2023, 13(5), 1086; https://doi.org/10.3390/agriculture13051086 - 19 May 2023
Cited by 4 | Viewed by 1594
Abstract
The demand for high-quality apples remains strong throughout the year, as they are one of the top three most popular fruits globally. However, the apple industry faces challenges in monitoring and managing postharvest losses due to invasive pests during long-term storage. In this [...] Read more.
The demand for high-quality apples remains strong throughout the year, as they are one of the top three most popular fruits globally. However, the apple industry faces challenges in monitoring and managing postharvest losses due to invasive pests during long-term storage. In this study, the effect of codling moth (CM) (Cydia pomonella [Linnaeus, 1758]), one of the most detrimental pests of apples, on the quality of the fruit was investigated under different storage conditions. Specifically, Gala apples were evaluated for their qualities such as firmness, pH, moisture content (MC), and soluble solids content (SSC). Near-infrared hyperspectral imaging (HSI) was implemented to build machine learning models for predicting the quality attributes of this apple during a 20-week storage using partial least squares regression (PLSR) and support vector regression (SVR) methods. Data were pre-processed using Savitzky–Golay smoothing filter and standard normal variate (SNV) followed by removing outliers by Monte Carlo sampling method. Functional analysis of variance (FANOVA) was used to interpret the variance in the spectra with respect to the infestation effect. FANOVA results showed that the effects of infestation on the near infrared (NIR) spectra were significant at p < 0.05. Initial results showed that the quality prediction models for the apples during cold storage at three different temperatures (0 °C, 4 °C, and 10 °C) were very high with a maximum correlation coefficient of prediction (Rp) of 0.92 for SSC, 0.95 for firmness, 0.97 for pH, and 0.91 for MC. Furthermore, the competitive adaptive reweighted sampling (CARS) method was employed to extract effective wavelengths to develop multispectral models for fast real-time prediction of the quality characteristics of apples. Model analysis showed that the multispectral models had better performance than the corresponding full wavelengths HSI models. The results of this study can help in developing non-destructive monitoring and evaluation systems for apple quality under different storage conditions. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

12 pages, 2851 KiB  
Article
Non-Destructive Appraisal of Macro- and Micronutrients in Persimmon Leaves Using Vis/NIR Hyperspectral Imaging
by Maylin Acosta, Isabel Rodríguez-Carretero, José Blasco, José Miguel de Paz and Ana Quiñones
Agriculture 2023, 13(4), 916; https://doi.org/10.3390/agriculture13040916 - 21 Apr 2023
Cited by 3 | Viewed by 3274
Abstract
Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The [...] Read more.
Visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI) was used for rapid and non-destructive determination of macro- and micronutrient contents in persimmon leaves. Hyperspectral images of 687 leaves were acquired in the 500–980 nm range over 6 months, covering a complete vegetative cycle. The average reflectance spectrum of each leaf was extracted, and foliar ionomic analysis was used as a reference method to determine the actual concentration of the nutrients in the leaves. Analyses were performed via emission spectrometry (ICP-OES) for macro- and micronutrients after microwave digestion and using the Kjeldahl method to quantify nitrogen. Partial least square regression (PLS-R) was used to predict the nutrient concentration based on spectral data from the leaf using actual values of each element as predictor variables. Several methods were used to pre-process the spectra, including Savitzky–Golay (SG) smoothing, standard normal variate (SNV) and first (1D) and second derivatives (2D). Seventy-five percent of the samples were used to calibrate and validate the model by cross-validation, whereas the remaining twenty-five % were used as an independent test set. The best performance of the models for the test set achieved an R2 = 0.80 for nitrogen. Results were also satisfactory for phosphorous, calcium, magnesium and boron, with determination coefficient R2 values of 0.63, 0.66, 0.58 and 0.69, respectively. For the other nutrients, lower prediction rates were attained (R2 = 0.48 for potassium, R2 = 0.38 for iron, R2 = 0.24 for copper, R2 = 0.23 for zinc and R2 = 0.22 for manganese). The variable importance in projection (VIP) was used to extract the most influential bands for the best-predicted nutrients, which were N, K and B. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

19 pages, 2478 KiB  
Article
Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data
by Oliver Persson Bogdanovski, Christoffer Svenningsson, Simon Månsson, Andreas Oxenstierna and Alexandros Sopasakis
Agriculture 2023, 13(4), 813; https://doi.org/10.3390/agriculture13040813 - 31 Mar 2023
Viewed by 2078
Abstract
We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography [...] Read more.
We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available. During training, we used information on winter wheat production over the fields of interest which was available from participating farmers. The two machine learning models we trained are the Light Gradient-Boosting Machine and a Feed-forward Neural Network. Our results show that Sentinel-1 data contain valuable information which can be used for training to predict winter wheat yield once two important steps are taken: performing a critical transformation of each pixel in the images to align it to the training data and then following up with despeckling treatment. Using this approach, we were able to achieve a top root mean square error of 0.75 tons per hectare and a top accuracy of 86% using a k-fold method with k=5. More importantly, however, we established that Sentinel-1 data alone are sufficient to predict yield with an average root mean square error of 0.89 tons per hectare, making this method feasible to employ worldwide. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

20 pages, 4207 KiB  
Article
On Precision Agriculture: Enhanced Automated Fruit Disease Identification and Classification Using a New Ensemble Classification Method
by Abid Mehmood, Muneer Ahmad and Qazi Mudassar Ilyas
Agriculture 2023, 13(2), 500; https://doi.org/10.3390/agriculture13020500 - 20 Feb 2023
Cited by 4 | Viewed by 2421
Abstract
Fruits are considered among the most nutrient-dense cash crops around the globe. Since fruits come in different types, sizes, shapes, colors, and textures, the manual classification and disease identification of a large quantity of fruit is time-consuming and sluggish, requiring massive human intervention. [...] Read more.
Fruits are considered among the most nutrient-dense cash crops around the globe. Since fruits come in different types, sizes, shapes, colors, and textures, the manual classification and disease identification of a large quantity of fruit is time-consuming and sluggish, requiring massive human intervention. We propose a multilevel fusion method for fruit disease identification and fruit classification that includes intensive fruit image pre-processing, customized image kernels for feature extraction with state-of-the-art (SOTA) deep methods, Gini-index-based controlled feature selection, and a hybrid ensemble method for identification and classification. We noticed certain limitations in the existing literature of adopting a single data source, in terms of limited data sizes, variability in fruit types, variability in quality, and variability in disease type. Therefore, we extensively aggregated and pre-processed multi-fruit data to simulate our proposed ensemble model on comprehensive datasets to cover both fruit classification and disease identification aspects. The multi-fruit imagery data contained regular and augmented images of fruits including apple, apricot, avocado, banana, cherry, fig, grape, guava, kiwi, mango, orange, peach, pear, pineapple, and strawberry. Similarly, we considered normal and augmented images of rotten fruits including beans (two categories), strawberries (seven categories), and tomatoes (three categories). For consistency, we normalized the images and designed an auto-labeling mechanism based on the existing image clusters to label inconsistent data to appropriate classes. Finally, we verified the auto-labeled data with a complete inspection to correctly assign it to the relevant classes. The proposed ensemble classifier outperforms all other classification methods, achieving 100% and 99% accuracy for fruit classification and disease identification. Further, we performed the analysis of variance (ANOVA) test to validate the statistical significance of the classifiers’ outcomes at α = 0.05. We achieved F-values of 32.41 and 11.42 against F-critical values of 2.62 and 2.86, resulting in p-values of 0.00 (<0.05) for fruit classification and disease identification. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

20 pages, 3913 KiB  
Article
BAG: A Linear-Nonlinear Hybrid Time Series Prediction Model for Soil Moisture
by Guoying Wang, Lili Zhuang, Lufeng Mo, Xiaomei Yi, Peng Wu and Xiaoping Wu
Agriculture 2023, 13(2), 379; https://doi.org/10.3390/agriculture13020379 - 04 Feb 2023
Cited by 4 | Viewed by 1658
Abstract
Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear problems and only predicts data with a single [...] Read more.
Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear problems and only predicts data with a single column of time series. The gated recurrent unit neural network (GRU) can achieve the prediction of time series and nonlinear multivariate data, but a single nonlinear model does not yield optimal results. Therefore, a hybrid time series prediction model, BAG, combining linear and nonlinear characteristics of soil moisture, is proposed in this paper to achieve the identification process of linear and nonlinear relationships in soil moisture data so as to improve the accuracy of prediction results. In BAG, block Hankel tensor ARIMA (BHT-ARIMA) and GRU are selected to extract the linear and nonlinear features of soil moisture data, respectively. BHT-ARIMA is applied to predict the linear part of the soil moisture, and GRU is used to predict the residual series, which is the nonlinear part, and the superposition of the two predicted results is the final prediction result. The performance of the proposed model on five real datasets was evaluated. The results of the experiments show that BAG has a higher prediction accuracy compared with other prediction models for different amounts of data and different numbers of environmental factors. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

16 pages, 5440 KiB  
Article
A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection
by Jinbo Zhou, Shan Zeng, Yulong Chen, Zhen Kang, Hao Li and Zhongyin Sheng
Agriculture 2023, 13(1), 182; https://doi.org/10.3390/agriculture13010182 - 11 Jan 2023
Cited by 4 | Viewed by 2291
Abstract
The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of [...] Read more.
The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

16 pages, 2363 KiB  
Article
Identification of Optimal Starting Time Instance to Forecast Net Blotch Density in Spring Barley with Meteorological Data in Finland
by Outi Ruusunen, Marja Jalli, Lauri Jauhiainen, Mika Ruusunen and Kauko Leiviskä
Agriculture 2022, 12(11), 1939; https://doi.org/10.3390/agriculture12111939 - 17 Nov 2022
Viewed by 1093
Abstract
The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. [...] Read more.
The performance of meteorological data-based methods to forecast plant diseases strongly depends on temporal weather information. In this paper, a data analysis procedure is presented for finding the optimal starting time for forecasting net blotch density in spring barley based on meteorological data. For this purpose, changes in the information content of typically measured weather variables were systemically quantified in sliding time windows and with additionally generated mathematical transformations, namely with features. Signal-to-noise statistics were applied in a novel way as a metric for identifying the optimal starting time instance and the most important features to successfully distinguish between two net blotch densities during springtime itself. According to the results, the information content of meteorological data used in classifying between nine years with and four years without net blotch reached its maximum in Finnish weather conditions on the 41st day from the beginning of the growing season. Specifically, utilising weather data at 41–55 days from the beginning of the growing season maximises successful forecasting potential of net blotch density. It also seems that this time instance enables a linear classification task with a selected feature subset, since the averages of the metrics in two data groups differ statistically with a minimum 68% confidence level for nine days in a 14-day time window. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

16 pages, 5035 KiB  
Article
Sichuan Pepper Recognition in Complex Environments: A Comparison Study of Traditional Segmentation versus Deep Learning Methods
by Jinzhu Lu, Juncheng Xiang, Ting Liu, Zongmei Gao and Min Liao
Agriculture 2022, 12(10), 1631; https://doi.org/10.3390/agriculture12101631 - 07 Oct 2022
Cited by 3 | Viewed by 1950
Abstract
At present, picking Sichuan pepper is mainly undertaken by people, which is inefficient and presents the possibility of workers getting hurt. It is necessary to develop an intelligent robot for picking Sichuan peppers in which the key technology is accurate segmentation by means [...] Read more.
At present, picking Sichuan pepper is mainly undertaken by people, which is inefficient and presents the possibility of workers getting hurt. It is necessary to develop an intelligent robot for picking Sichuan peppers in which the key technology is accurate segmentation by means of mechanical vision. In this study, we first took images of Sichuan peppers (Hanyuan variety) in an orchard under various conditions of light intensity, cluster numbers, and image occlusion by other elements such as leaves. Under these various image conditions, we compared the ability of different technologies to segment the images, examining both traditional image segmentation methods (RGB color space, HSV color space, k-means clustering algorithm) and deep learning algorithms (U-Net convolutional network, Pyramid Scene Parsing Network, DeeplabV3+ convolutional network). After the images had been segmented, we compared the effectiveness of each algorithm at identifying Sichuan peppers in the various types of image, using the Intersection Over Union(IOU) and Mean Pixel Accuracy(MPA) indexes to measure success. The results showed that the U-Net algorithm was the most effective in the case of single front-lit clusters light without occlusion, with an IOU of 87.23% and an MPA of 95.95%. In multiple front-lit clusters without occlusion, its IOU was 76.52% and its MPA was 94.33%. Based on these results, we propose applicable segmentation methods for an intelligent Sichuan pepper-picking robot which can identify the fruit in images from various growing environments. The research showed good accuracy for the recognition and segmentation of Sichuan peppers, which suggests that this method can provide technical support for the visual recognition of a pepper-picking robot in the field. Full article
(This article belongs to the Special Issue Applications of Data Analysis in Agriculture)
Show Figures

Figure 1

Back to TopTop