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Applications, Challenges and Potential of Intelligent Equipment in Agriculture

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 15212

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Prof. Dr. Daniel García Fernández-Pacheco

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Department of Structures, Construction and Graphic Expression, Universidad Politécnica de Cartagena, 30202 Cartagena, Murcia, Spain
Interests: computer vision; multiagent systems; agricultural and biological sciences; modelling of biological structures
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Prof. Dr. José Miguel Molina Martínez

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Department of Agricultural Engineering, Technical University of Cartagena, 30202 Cartagena, Murcia, Spain
Interests: water resources management; irrigation; energy efficiency; smart agriculture; agriculture automation and control; computers and electronics in agriculture
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Dr. Dolores Parras-Burgos

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Department of Structures, Construction and Graphic Expression, Universidad Politécnica de Cartagena, 30202 Cartagena, Murcia, Spain
Interests: industrial design in agriculture; augmented/virtual reality; CAD
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, there is a need to continue generating multidisciplinary knowledge with the aim of offering researchers, technicians and agricultural entrepreneurs an updated view of the various tools and technologies available: remote sensing, sensors, robotics, modelling, artificial intelligence, data acquisition and visualisation, IoT communications, etc. These tools and technologies can help to solve global challenges and problems, such as the optimisation of natural resources/soil/water, the improvement of human resources/mechanised harvesting, and reducing the effect of climate change, all of which pose a threat to the great diversity of crops covered by current horticulture.

This Special Issue invites you to participate in the research and technological developments via the submission of original papers, in order to advance the management of horticultural tools and technologies such as: (i) mechanised harvesting, (ii) precision agriculture, (iii) innovative strategies, digital transformation and irrigation optimisation through ICTs, (iv) sensorisation, and (v) remote sensing and machine learning applications in satellite images and UAVs in different spectral ranges, among others.

Dr. Daniel García Fernández-Pacheco
Prof. Dr. José Miguel Molina Martínez
Dr. Dolores Parras-Burgos
Guest 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 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

smart agriculture
agricultural digitalisation
agricultural robotics
precision farming
irrigation and fertigation management
software applications
agro-industrial automation
mechatronics and computing

Published Papers (9 papers)

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Research

10 pages, 1232 KiB

Open AccessArticle

Estimation of Macro and Micronutrients in Persimmon (Diospyros kaki L.) cv. ‘Rojo Brillante’ Leaves through Vis-NIR Reflectance Spectroscopy

by Maylin Acosta, Fernando Visconti, Ana Quiñones, José Blasco and José Miguel de Paz

Agronomy 2023, 13(4), 1105; https://doi.org/10.3390/agronomy13041105 - 12 Apr 2023

Cited by 2 | Viewed by 2099

Abstract

The nutritional diagnosis of crops is carried out through costly elemental analyses of different plant organs, particularly leaves, in the laboratory. However, visible and near-infrared (Vis-NIR) spectroscopy of unprocessed plant samples has a high potential as a faster, non-destructive, environmental-friendly alternative to elemental analyses. In this work, the potential of this technique to estimate the concentrations of macronutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), and micronutrients such as iron (Fe), manganese (Mn) and boron (B), in persimmon (Diospyros kaki L.) ‘Rojo Brillante’ leaves, has been investigated. Throughout the crop cycle variable rates of N and K were applied to obtain six nutritional status levels in persimmon trees in an experimental orchard. Then, leaves were systematically sampled throughout the cropping season from the different nutritional levels and spectral reflectance measurements were acquired in the 430–1040 nm wavelength range. The concentrations of nutrients were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) for P, K, Ca, Mg, Fe, Mn and B after microwave digestion, while the Kjeldahl method was used for N. Then, partial least squares regression (PLS-R) was used to model the concentrations of these nutrients from the reflectance measurements of the leaves. The model was calibrated using 75% of the samples while the remaining 25% were left as the independent test set for external validation. The results of the test set indicated an acceptable validation for most of the nutrients, with determination coefficients (R²) of 0.74 for N and P, 0.54 for K, 0.77 for Ca, 0.60 for Mg, 0.39 for Fe, 0.69 for Mn and 0.83 for B. These findings support the potential use of Vis-NIR spectrometric techniques as an alternative to conventional laboratory methods for the persimmon nutritional status diagnosis although more research is needed to know how the models developed one year perform in ensuing years. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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22 pages, 11120 KiB

Open AccessArticle

Selection of a Navigation Strategy According to Agricultural Scenarios and Sensor Data Integrity

by Leonardo Bonacini, Mário Luiz Tronco, Vitor Akihiro Hisano Higuti, Andres Eduardo Baquero Velasquez, Mateus Valverde Gasparino, Handel Emanuel Natividade Peres, Rodrigo Praxedes de Oliveira, Vivian Suzano Medeiros, Rouverson Pereira da Silva and Marcelo Becker

Agronomy 2023, 13(3), 925; https://doi.org/10.3390/agronomy13030925 - 21 Mar 2023

Cited by 1 | Viewed by 1325

Abstract

In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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16 pages, 6137 KiB

Open AccessArticle

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

by Piotr Mazur, Dariusz Gozdowski, Wojciech Stępień and Elżbieta Wójcik-Gront

Agronomy 2023, 13(2), 446; https://doi.org/10.3390/agronomy13020446 - 02 Feb 2023

Viewed by 1452

Abstract

The evaluation of the nutrient content in the soil, such as potassium and phosphorus, is very important, especially in precision agriculture, where the fertilizer rate should be adjusted to field variability in terms of nutrient content. Soil chemical analysis is time-consuming and expensive, and dense soil sampling is not always possible. In recent years, remote sensing methods have been used to assess the within-field variability of soil and crop nutritional status. The main aim of this study was to evaluate the relationship between UAV-derived spectral reflectance for winter rye in a long-term experiment for different fertilization with phosphorus and potassium. The study was conducted in 2022 in two field experiments in which winter rye was cultivated in monoculture and with crop rotation. The experiments were located in central Poland in Skierniewice. Statistical analyses were performed using univariate and multivariate methods, e.g., analysis of correlation, regression, and principal component analysis (PCA). The effect of phosphorus and potassium fertilization on the UAV-derived spectral reflectance of winter rye was weak, weaker in comparison to the effect of nitrogen fertilization. The effect of phosphorus and potassium fertilization on spectral reflectance was stronger in the experiment with monoculture than in the experiment with crop rotation. On the basis of correlation coefficients and PCA, negative relationships were proven between available soil potassium and spectral reflectance in the range of blue, green, and red bands and positive with red edge and near-infrared bands. The first principal component (PC1) was very strongly correlated with almost all spectral bands, either positively or negatively. The correlation of potassium and phosphorus content was very weak with PC1 in the experiment with crop rotation, while in the experiment with rye monoculture, the correlation was slightly stronger, indicating a stronger effect of nutrient deficiency in monoculture. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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14 pages, 2169 KiB

Open AccessArticle

Utilisation of Deep Learning with Multimodal Data Fusion for Determination of Pineapple Quality Using Thermal Imaging

by Maimunah Mohd Ali, Norhashila Hashim, Samsuzana Abd Aziz and Ola Lasekan

Agronomy 2023, 13(2), 401; https://doi.org/10.3390/agronomy13020401 - 30 Jan 2023

Cited by 6 | Viewed by 1816

Abstract

Fruit quality is an important aspect in determining the consumer preference in the supply chain. Thermal imaging was used to determine different pineapple varieties according to the physicochemical changes of the fruit by means of the deep learning method. Deep learning has gained attention in fruit classification and recognition in unimodal processing. This paper proposes a multimodal data fusion framework for the determination of pineapple quality using deep learning methods based on the feature extraction acquired from thermal imaging. Feature extraction was selected from the thermal images that provided a correlation with the quality attributes of the fruit in developing the deep learning models. Three different types of deep learning architectures, including ResNet, VGG16, and InceptionV3, were built to develop the multimodal data fusion framework for the classification of pineapple varieties based on the concatenation of multiple features extracted by the robust networks. The multimodal data fusion coupled with powerful convolutional neural network architectures can remarkably distinguish different pineapple varieties. The proposed multimodal data fusion framework provides a reliable determination of fruit quality that can improve the recognition accuracy and the model performance up to 0.9687. The effectiveness of multimodal deep learning data fusion and thermal imaging has huge potential in monitoring the real-time determination of physicochemical changes of fruit. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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11 pages, 3146 KiB

Open AccessArticle

Geostatistical Methods to Build Citrus Cross-Pollination Risk Maps

by Enrique Moltó, Carmen Orts, José L. Pardo and Héctor Izquierdo-Sanz

Agronomy 2022, 12(11), 2673; https://doi.org/10.3390/agronomy12112673 - 28 Oct 2022

Viewed by 968

Abstract

Valencian citriculture is oriented towards fresh production, which requires fruits with few seeds or seedless fruits. Consequently, parthenocarpic and self-incompatible varieties are mainly cultivated. However, some mandarin varieties, under favorable circumstances, induce seed formation in other mandarins by cross-pollination. This phenomenon depends on the germination capacity of the pollen of the pollinating variety, the number of ovules of the pollinated variety, the distance between them, and the abundance of pollinating insects. Previous studies in Instituto Valenciano de Investigaciones Agrarias (IVIA) have determined the ability to pollinate and be pollinated by all commercial varieties in Europe. Moreover, the Regional Government, Generalitat Valenciana, has georeferenced information on the cultivated varieties. We present two geostatistical models to estimate the risk of plots to be pollinated, depending on the varieties present in their environment, the number of plants, and their distance. Models are used to generate local and regional cross-pollination risk maps. Moreover, the robustness of these models to changes in the values assigned to their main parameters is assessed using different similarity calculations. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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9 pages, 2522 KiB

Open AccessCommunication

Low-Cost Electronic Nose for Wine Variety Identification through Machine Learning Algorithms

by Agustin Conesa Celdrán, Martin John Oates, Carlos Molina Cabrera, Chema Pangua, Javier Tardaguila and Antonio Ruiz-Canales

Agronomy 2022, 12(11), 2627; https://doi.org/10.3390/agronomy12112627 - 25 Oct 2022

Cited by 7 | Viewed by 1745

Abstract

The aroma of wine is traditionally analyzed by sensory methods or by using gas chromatography; both analytical methodologies are slow and expensive and do not allow continuous monitoring. For this reason, interest in rapid methods has increased in recent times. Electronic noses (e-noses) stand out for their high sensitivity, speed, low cost, and little or no sample preparation. They present, however, low selectivity, which requires advance analytical methods to distinguish compounds. Here, we present a low-cost e-nose device for the analysis and identification of distinct varieties of wine. Chemical analysis data are compared to e-nose data through a principal component analysis (PCA) and a k-means clustering algorithm to establish relationships between varieties of wines and the e-nose classification capability. The results show that e-nose technology found significant differences between the analyzed samples, and furthermore, classifying the samples in accordance with the chemical analysis classification. The maximal accuracy obtained was 100% using the k-means algorithm for binary classification with N = 21 samples. Thus the potential of e-nose technology was shown in the wine industry for the identification and classification of wine varieties or quality. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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17 pages, 2044 KiB

Open AccessArticle

Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge

by Asier Uribeetxebarria, Ander Castellón, Ibai Elorza and Ana Aizpurua

Agronomy 2022, 12(10), 2276; https://doi.org/10.3390/agronomy12102276 - 23 Sep 2022

Cited by 2 | Viewed by 1613

Abstract

The variable fertilization rate (VFR) technique has demonstrated its ability to reduce nutrient losses by adapting the fertilizer dose to crop needs. However, transferring this technology to farms is not easy. This study aimed to make a variable fertilization map in a commercial plot where there is no data from a yield monitor, combining machine learning techniques and farmer’s knowledge. In addition to the normalized difference vegetation index (NDVI) obtained from Sentinel-2 and a digital elevation model (DEM), information captured by a yield monitor in 2019 was used to train and validate models. Among the 15 algorithms trained, the best result was obtained by the random forest (RF), with an RMSE of 496 and R² of 0.90. Using the “leave one out” technique, the capacity to predict an entire plot was tested. Finally, the RF algorithm was tested on a 12-hectare wheat plot where no yield data were available. The novelty of this work lies in the collaborative work developed between farmers and researchers to implement the VRF technique in plots where precise yield data do not exist and in the “leave one out” validation. The collaboration between scientists and farmers resulted in a very positive exchange of information that allowed the farmer to change the fertilization strategy of the whole farm and the scientists to better understand how soil properties and plot history affect yield. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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27 pages, 16316 KiB

Open AccessArticle

Design and Optimization of a Machine-Vision-Based Complementary Seeding Device for Tray-Type Green Onion Seedling Machines

by Junpeng Gao, Yuhua Li, Kai Zhou, Yanqiang Wu and Jialin Hou

Agronomy 2022, 12(9), 2180; https://doi.org/10.3390/agronomy12092180 - 14 Sep 2022

Cited by 2 | Viewed by 1690

Abstract

Green onion (Allium fistulosum L.) is mainly available as factory-produced seedlings. Although factory seedling production is highly automated, miss-seeding during the seeding process considerably affects subsequent transplanting and the final yield. To solve the problem of miss-seeding, the current main method is manual complementary seeding, which is labor-intensive and inefficient work. In this study, an automatic machine-vision-based complementary seeding device was proposed to reduce the miss-seeding rate and as a replacement of manual complementary seeding. The device performs several main functions, including the identification of miss-seeding holes, control of seed case movement, and the seed uptake and release from the seed suction nozzle array. A majority-mechanism-based miss-seeding tray hole rapid-detection method was proposed to enable the real-time identification of miss-seeding tray holes in the tray under high-speed moving conditions. The structural parameters of the vacuum-generated seed suction nozzle were optimized through numerical simulations and orthogonal experiments, and the seed suction nozzle array and seed case were produced using 3D-printing technology. Finally, the complementary seeding device was installed on the tray-type green onion seeding machine and the effectiveness of the complementary seeding was confirmed by experiments. The results revealed that the average values of the precision, recall, and F1 scores for identifying miss-seeding tray holes were 98.48%, 97.00%, and 97.73%, respectively. The results revealed that the rate of miss-seeding tray holes decreased from 5.37% to 0.89% after complementary seeding. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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14 pages, 2161 KiB

Open AccessArticle

Classification of Monofloral Honeys by Measuring Electrical Impedance Based on Neural Networks

by Eduardo González María, Antonio Madueño-Luna, Antonio Ruiz-Canales and José Miguel Madueño Luna

Agronomy 2022, 12(8), 1929; https://doi.org/10.3390/agronomy12081929 - 17 Aug 2022

Cited by 2 | Viewed by 1478

Abstract

The study of electrical impedance applied to food has become a method with great potential for use in the food industry, which allows the monitoring and control of quality processes in a safe and non-invasive way. Recent research has shown that this technique can be an alternative method to determine the floral origin of the honey bee (Apis mellifera L.) and acquire information on chemical and physical properties such as conductivity, ash content and acidity. In this work, the electrical impedance of six monofloral honey samples from diverse origins and one commercial multi-floral honey were measured using a low-cost impedance meter, obtaining 101 samples (reactance (X) versus resistance (R)), with a frequency sweep between 1 Hz and 25 MHz in all the honeys analyzed. This shows that it is possible, by using a multilayer neural network trained from these data, to classify with 100% accuracy between these honeys and, thereby, quickly and easily determine the floral origin of the honey. This is without the need to use the chemical data or equivalent electrical models. Full article

(This article belongs to the Special Issue Applications, Challenges and Potential of Intelligent Equipment in Agriculture)

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