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Agronomy
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Application of Image Processing in Agriculture
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Application of Image Processing in 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 2023) | Viewed by 40345

Special Issue Editor

Prof. Dr. Antonio Maria Garcia Tommaselli

E-Mail Website
Guest Editor
Department of Cartography, São Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil
Interests: photogrammetry; machine vision; camera calibration; sensors’ integration

Special Issue Information

Dear Colleagues,

Agriculture is rapidly incorporating new techniques from a number of research domains with the aim of improving sustainability and productivity. One example of this is image processing, a discipline that plays an important role in the management of the majority of agronomic systems. Images are remotely collected and processed with high frequency, providing spatial resolution according to the user’s requirements. With the dissemination of low-cost and high-resolution optical sensors, the range of applications is growing rapidly, requiring more research to cope with these types of data. The miniaturization and cost reduction of multispectral and hyperspectral sensors also introduced new perspectives for applications in agriculture.

This Special Issue aims to cover this broad research field of image processing dedicated to agronomy. Focus will be given to new techniques for processing high-resolution images collected with RGB, multispectral and hyperspectral sensors from the air (with UAVs, for instance) or from the ground to solve agricultural problems. Papers on topics such as real-time processing techniques of optical images collected with low-cost devices (e.g, smartphones), computer vision and machine learning techniques, and combinations of optical images with other sources of data, e.g., LiDar point clouds and optical navigation, are also welcome.

Prof. Dr. Antonio Maria Garcia Tommaselli
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.

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Editorial

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3 pages, 197 KiB  
Editorial
Application of Image Processing in Agriculture
by Antonio Maria Garcia Tommaselli
Agronomy 2023, 13(9), 2399; https://doi.org/10.3390/agronomy13092399 - 17 Sep 2023
Viewed by 2185
Abstract
Agriculture will face significant challenges in the 21st century to feed a record number of people and animals and generate resources for industry (for example, wood, cellulose, and energy); thus, it is essential increasing yield and reducing pollution, water consumption, and energy consumption [...] Read more.
Agriculture will face significant challenges in the 21st century to feed a record number of people and animals and generate resources for industry (for example, wood, cellulose, and energy); thus, it is essential increasing yield and reducing pollution, water consumption, and energy consumption [...] Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)

Research

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20 pages, 4519 KiB  
Article
Design of Vegetation Index for Identifying the Mosaic Virus in Sugarcane Plantation: A Brazilian Case Study
by Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Eija Honkavaara and David Luciano Rosalen
Agronomy 2023, 13(6), 1542; https://doi.org/10.3390/agronomy13061542 - 1 Jun 2023
Cited by 3 | Viewed by 1637
Abstract
Phytosanitary control of crops requires the rapid mapping of diseases to enable management attention. This study aimed to evaluate the potential of vegetation indices for the detection of sugarcane mosaic disease. Spectral indices were applied to hyperspectral images collected by an unmanned aerial [...] Read more.
Phytosanitary control of crops requires the rapid mapping of diseases to enable management attention. This study aimed to evaluate the potential of vegetation indices for the detection of sugarcane mosaic disease. Spectral indices were applied to hyperspectral images collected by an unmanned aerial vehicle (UAV) to find the areas affected by the mosaic virus in sugarcane. Identifying indices capable of detecting diseased plants in agricultural crops supports data processing and the development of efficient tools. A new index was designed based on spectral regions, which presents higher differences between healthy and mosaic virus-infected leaves to enhance hyperspectral image pixels representing diseased plants. Based on the data generated, we propose the anthocyanin red edge index (AREI) for mosaic virus detection in sugarcane plantations. An index that can adequately identify sugarcane infected by the mosaic virus may incorporate wavelengths associated with variations in leaf pigment concentrations as well as changes in leaf structure. The indices that assessed to detect plants infected with the sugarcane mosaic virus were the normalised difference vegetation index (NDVI), normalised difference vegetation index red edge (NDVI705), new vegetation index (NVI), ARI2 and AREI. The results showed that AREI presented the best performance for the detection of mosaic in sugarcane from UAV images, giving an overall accuracy of 0.94, a kappa coefficient of 0.87, and omission and inclusion errors of 2.86% and 10.52%, respectively. The results show the importance of wavelengths associated with the concentration of chlorophyll and anthocyanin and the position of the red edge for the detection of diseases in sugarcane. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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17 pages, 2937 KiB  
Article
Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?
by Roope Näsi, Hannu Mikkola, Eija Honkavaara, Niko Koivumäki, Raquel A. Oliveira, Pirjo Peltonen-Sainio, Niila-Sakari Keijälä, Mikael Änäkkälä, Lauri Arkkola and Laura Alakukku
Agronomy 2023, 13(3), 669; https://doi.org/10.3390/agronomy13030669 - 25 Feb 2023
Cited by 6 | Viewed by 2053
Abstract
Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been [...] Read more.
Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R2 > 0.5), especially for annual crops, and in the best case, the R2 value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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15 pages, 1482 KiB  
Article
Estimating Coffee Plant Yield Based on Multispectral Images and Machine Learning Models
by Carlos Alberto Matias de Abreu Júnior, George Deroco Martins, Laura Cristina Moura Xavier, Bruno Sérgio Vieira, Rodrigo Bezerra de Araújo Gallis, Eusimio Felisbino Fraga Junior, Rafaela Souza Martins, Alice Pedro Bom Paes, Rafael Cordeiro Pereira Mendonça and João Victor do Nascimento Lima
Agronomy 2022, 12(12), 3195; https://doi.org/10.3390/agronomy12123195 - 16 Dec 2022
Cited by 3 | Viewed by 1816
Abstract
The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, [...] Read more.
The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. Yield data from a same study area in 2017, 2018 and 2019, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The NN algorithm was also more accurate (27% RMSE) in predicting yield. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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20 pages, 16750 KiB  
Article
Edge-Compatible Deep Learning Models for Detection of Pest Outbreaks in Viticulture
by João Gonçalves, Eduardo Silva, Pedro Faria, Telmo Nogueira, Ana Ferreira, Cristina Carlos and Luís Rosado
Agronomy 2022, 12(12), 3052; https://doi.org/10.3390/agronomy12123052 - 2 Dec 2022
Cited by 5 | Viewed by 1985
Abstract
The direct effect of global warming on viticulture is already apparent, with unexpected pests and diseases as one of the most concerning consequences. Deploying sticky traps on grape plantations to attract key insects has been the backbone of conventional pest management programs. However, [...] Read more.
The direct effect of global warming on viticulture is already apparent, with unexpected pests and diseases as one of the most concerning consequences. Deploying sticky traps on grape plantations to attract key insects has been the backbone of conventional pest management programs. However, they are time-consuming processes for winegrowers, conducted through visual inspection via the manual identification and counting of key insects. Additionally, winegrowers usually lack taxonomy expertise for accurate species identification. This paper explores the usage of deep learning on the edge to identify and quantify pest counts automatically. Different mobile devices were used to acquire a dataset of yellow sticky and delta traps, consisting of 168 images with 8966 key insects manually annotated by experienced taxonomy specialists. Five different deep learning models suitable to run locally on mobile devices were selected, trained, and benchmarked to detect five different insect species. Model-centric, data-centric, and deployment-centric strategies were explored to improve and fine-tune the considered models, where they were tested on low-end and high-end mobile devices. The SSD ResNet50 model proved to be the most suitable architecture for deployment on edge devices, with accuracies per class ranging from 82% to 99%, the F1 score ranging from 58% to 84%, and inference speeds per trap image of 19.4 s and 62.7 s for high-end and low-end smartphones, respectively. These results demonstrate the potential of the approach proposed to be integrated into a mobile-based solution for vineyard pest monitoring by providing automated detection and the counting of key vector insects to winegrowers and taxonomy specialists. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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17 pages, 9113 KiB  
Article
Determination of Moisture in Rice Grains Based on Visible Spectrum Analysis
by Héctor Palacios-Cabrera, Karina Jimenes-Vargas, Mario González, Omar Flor-Unda and Belén Almeida
Agronomy 2022, 12(12), 3021; https://doi.org/10.3390/agronomy12123021 - 29 Nov 2022
Cited by 3 | Viewed by 4221
Abstract
Rice grain production is important for the world economy. Determining the moisture content of the grains, at several stages of production, is crucial for controlling the quality, safety, and storage of the grain. This work inspects how well rice images from global and [...] Read more.
Rice grain production is important for the world economy. Determining the moisture content of the grains, at several stages of production, is crucial for controlling the quality, safety, and storage of the grain. This work inspects how well rice images from global and local descriptors work for determining the moisture content of the grains using artificial vision and intelligence techniques. Three sets of images of rice grains from the INIAP 12 variety (National Institute of Agricultural Research of Ecuador) were captured with a mobile camera. The first one with natural light and the other ones with a truncated pyramid-shaped structure. Then, a set of global descriptors (color, texture) and a set of local descriptors (AZAKE, BRISK, ORB, and SIFT) in conjunction with the dominate technique bag of visual words (BoVW) were used to analyze the content of the image with classification and regression algorithms. The results show that detecting humidity through images with classification and regression algorithms is possible. Finally, f1-score values of at least 0.9 were accomplished for global color descriptors and of 0.8 for texture descriptors, in contrast to the local descriptors (AKAZE, BRISK, and SIFT) that reached up to an f1-score of 0.96. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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17 pages, 3469 KiB  
Article
Early Detection of Coffee Leaf Rust Caused by Hemileia vastatrix Using Multispectral Images
by Analis da Silva Soares, Bruno Sérgio Vieira, Thalita Almeida Bezerra, George Deroco Martins and Ana Carolina Silva Siquieroli
Agronomy 2022, 12(12), 2911; https://doi.org/10.3390/agronomy12122911 - 22 Nov 2022
Cited by 6 | Viewed by 2153
Abstract
Conventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants [...] Read more.
Conventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants worldwide, using multispectral images acquired using a Mapir Survey3W camera and an unmanned aerial vehicle (UAV). For this purpose, 160 coffee seedlings of the coffee cultivar ‘Mundo Novo’ were inoculated with urediniospores of H. vastatrix and compared with 160 control (non-inoculated) seedlings to determine the most favorable interval for distinguishing healthy and infected plants. The 320 seedlings were placed on a dark surface to perform the imaging flights. In vitro analyses of the physiological parameters of 20 specimens were then performed for each condition (inoculated/non-inoculated) to obtain the hyperspectral curves, and this process was repeated three times at 15, 30, and 45 days after inoculation (DAI). Based on the simulated hyperspectral curves, a discrepancy between the red and near-infrared (NIR) bands was identified at 15 DAI, with the inoculated plants showing greater absorption in the red band and a greater spectral response in the NIR band. Thus, multispectral images were able to distinguish H. vastatrix infection in coffee seedlings at an asymptomatic stage (15 DAI) using a support vector machines (SVM) algorithm. Detection accuracy was 80% and the Kappa index of agreement was moderate (0.6). The early detection of this pathogen in the field using low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of the pathogen, causing farmers to make applications of chemical fungicides only when necessary. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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15 pages, 25205 KiB  
Article
Insect Predation Estimate Using Binary Leaf Models and Image-Matching Shapes
by Gabriel S. Vieira, Afonso U. Fonseca, Bruno M. Rocha, Naiane M. Sousa, Julio C. Ferreira, Juliana P. Felix, Junio C. Lima and Fabrizzio Soares
Agronomy 2022, 12(11), 2769; https://doi.org/10.3390/agronomy12112769 - 7 Nov 2022
Cited by 5 | Viewed by 1688
Abstract
Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the [...] Read more.
Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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10 pages, 1663 KiB  
Article
Using Image Analysis and Regression Modeling to Develop a Diagnostic Tool for Peanut Foliar Symptoms
by Hope Renfroe-Becton, Kendall R. Kirk and Daniel J. Anco
Agronomy 2022, 12(11), 2712; https://doi.org/10.3390/agronomy12112712 - 1 Nov 2022
Cited by 3 | Viewed by 1458
Abstract
Peanut foliar diseases and disorders can be difficult to rapidly diagnose with little experience because some abiotic and biotic symptoms present similar symptoms. Developing algorithms for automated identification of peanut foliar diseases and disorders could potentially provide a quick, affordable, and easy method [...] Read more.
Peanut foliar diseases and disorders can be difficult to rapidly diagnose with little experience because some abiotic and biotic symptoms present similar symptoms. Developing algorithms for automated identification of peanut foliar diseases and disorders could potentially provide a quick, affordable, and easy method for diagnosing peanut symptoms. To examine this, images of peanut leaves were captured from various angles, distances, and lighting conditions using various cameras. Color space data from all images was subsequently extracted and subjected to logistic regression. Separate algorithms were developed for each symptom to include healthy, hopperburn, late leaf spot, Provost injury, tomato spotted wilt, paraquat injury, or surfactant injury. The majority of these symptoms are not included within currently available disease identification mobile apps. All of the algorithms developed for peanut foliar diagnostics were ≥ 86% accurate. These diagnostic algorithms have the potential to be a valuable tool for growers if made available via a web-accessible platform, which is the next step of this work. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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33 pages, 2785 KiB  
Article
Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods
by Sayantan Sarkar, Joseph Oakes, Alexandre-Brice Cazenave, Mark D. Burow, Rebecca S. Bennett, Kelly D. Chamberlin, Ning Wang, Melanie White, Paxton Payton, James Mahan, Jennifer Chagoya, Cheng-Jung Sung, David S. McCall, Wade E. Thomason and Maria Balota
Agronomy 2022, 12(8), 1945; https://doi.org/10.3390/agronomy12081945 - 18 Aug 2022
Cited by 8 | Viewed by 2146
Abstract
Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. [...] Read more.
Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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20 pages, 3403 KiB  
Article
Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging
by Fernando Henrique Iost Filho, Juliano de Bastos Pazini, André Dantas de Medeiros, David Luciano Rosalen and Pedro Takao Yamamoto
Agronomy 2022, 12(7), 1516; https://doi.org/10.3390/agronomy12071516 - 24 Jun 2022
Cited by 8 | Viewed by 3566
Abstract
Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool [...] Read more.
Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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13 pages, 4745 KiB  
Article
Silage Grass Sward Nitrogen Concentration and Dry Matter Yield Estimation Using Deep Regression and RGB Images Captured by UAV
by Raquel Alves Oliveira, José Marcato Junior, Celso Soares Costa, Roope Näsi, Niko Koivumäki, Oiva Niemeläinen, Jere Kaivosoja, Laura Nyholm, Hemerson Pistori and Eija Honkavaara
Agronomy 2022, 12(6), 1352; https://doi.org/10.3390/agronomy12061352 - 1 Jun 2022
Cited by 9 | Viewed by 2480
Abstract
Agricultural grasslands are globally important for food production, biodiversity, and greenhouse gas mitigation. Effective strategies to monitor grass sward properties, such as dry matter yield (DMY) and nitrogen concentration, are crucial when aiming to improve the sustainable use of grasslands in the context [...] Read more.
Agricultural grasslands are globally important for food production, biodiversity, and greenhouse gas mitigation. Effective strategies to monitor grass sward properties, such as dry matter yield (DMY) and nitrogen concentration, are crucial when aiming to improve the sustainable use of grasslands in the context of food production. UAV-borne spectral imaging and traditional machine learning methods have already shown the potential to estimate DMY and nitrogen concentration for the grass swards. In this study, convolutional neural networks (CNN) were trained using low-cost RGB images, captured from a UAV, and agricultural reference measurements collected in an experimental grass field in Finland. Four different deep regression network architectures and three different optimizers were assessed. The best average results of the cross-validation were achieved by the VGG16 architecture with optimizer Adadelta: r2 of 0.79 for DMY and r2 of 0.73 for nitrogen concentration. The results demonstrate that this is a promising and effective tool for practical applications since the sensor is low-cost and the computational processing is not time-consuming in comparison to more complex sensors. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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16 pages, 5197 KiB  
Article
The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
by Jardel da Silva Souza, Laura Monteiro Pedrosa, Bruno Rafael de Almeida Moreira, Elizanilda Ramalho do Rêgo and Sandra Helena Unêda-Trevisoli
Agronomy 2022, 12(6), 1342; https://doi.org/10.3390/agronomy12061342 - 31 May 2022
Cited by 3 | Viewed by 2079
Abstract
A breeder can select a visually appealing phenotype, whether for ornamentation or landscaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an [...] Read more.
A breeder can select a visually appealing phenotype, whether for ornamentation or landscaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 > 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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23 pages, 7316 KiB  
Article
Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine
by Alberto San Bautista, David Fita, Belén Franch, Sergio Castiñeira-Ibáñez, Patricia Arizo, María José Sánchez-Torres, Inbal Becker-Reshef, Antonio Uris and Constanza Rubio
Agronomy 2022, 12(3), 708; https://doi.org/10.3390/agronomy12030708 - 15 Mar 2022
Cited by 13 | Viewed by 3305
Abstract
World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation [...] Read more.
World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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15 pages, 10193 KiB  
Article
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces
by Bruno Rafael de Almeida Moreira, Armando Lopes de Brito Filho, Marcelo Rodrigues Barbosa Júnior and Rouverson Pereira da Silva
Agronomy 2022, 12(2), 446; https://doi.org/10.3390/agronomy12020446 - 10 Feb 2022
Cited by 1 | Viewed by 1408
Abstract
Surface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist [...] Read more.
Surface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist of an exceptional merger for high-throughput predictive analytics, we developed carbon-capturing biointerfaces by pelletizing hydrochars of sugarcane bagasse, pinewood sawdust, peanut pod hull, wheat straw, and peaty compost. The apochromatic stereomicroscopy captured outstanding micrographs of biointerfaces. Hence, it enabled the segmenting algorithm to distinguish between rough and smooth microstructural stresses by chromatic similarity and topological proximity. The box-counting algorithm then adequately determined the fractal dimension of microcracks, merely as a result of processing segments of the image, without any computational unfeasibility. The larger the fractal pattern, the more loss of functional gas-binding sites, namely N and S, and thus the potential sorption significantly decreases from 10.85 to 7.20 mmol CO2 g−1 at sigmoid Gompertz function. Our insights into analyzing fractal carbon-capturing biointerfaces provide forward knowledge of particular relevance to progress in the field’s prominence in bringing high-throughput methods into implementation to study adsorption towards upgrading carbon capture and storage (CCS) and carbon capture and utilization (CCU). Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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9 pages, 1297 KiB  
Article
Assessing Intra-Row Spacing Using Image Processing: A Promising Digital Tool for Smallholder Farmers
by Vinicius Dos Santos Carreira, Danilo Tedesco, Alexandre Dos Santos Carreira and Rouverson Pereira da Silva
Agronomy 2022, 12(2), 301; https://doi.org/10.3390/agronomy12020301 - 25 Jan 2022
Cited by 3 | Viewed by 3290
Abstract
Assessing planting to ensure well-distributed plants is important to achieve high yields. Digital farming has been helpful in these field assessments. However, these techniques are at most times not available for smallholder farmers or low-income regions. Thus, to contribute such producers, we developed [...] Read more.
Assessing planting to ensure well-distributed plants is important to achieve high yields. Digital farming has been helpful in these field assessments. However, these techniques are at most times not available for smallholder farmers or low-income regions. Thus, to contribute such producers, we developed two methods to assess intra-row spacing in commercial fields using mobile photos and simple image processing. We assessed a maize field after mechanized planting in 7 and 12 days after planting (DAP) and in two farming systems (conventional and no-till) to acquire images at height of one meter and perpendicular to the ground. In the first method, we used morphological operations based on the HSV scale and the center of mass to extract the region of interest (ROI) corresponding to the maize plant. In the second method, we used local maxima equations (Peaks) to find prominence values corresponding to the maize plant and extract their coordinates. No-till images were deleted due to excessive weeds. Thus, before acquiring the images, it is necessary to remove these elements (e.g., no-till adapted). The methods achieved an overall RMSE of 3.48 cm (<5.63 cm) and R² of 0.90 (>0.71) between the actual and estimated spacing. Precision and recall were higher than 0.88. There was no difference between actual and estimated CV values, except in conventional tillage in 7 DAP using ROI due to leaves overlapping. The method Peaks was more accurate to detect multiple spacing but miss spacing was correctly detected in both methods. However, the larger the plant leaves, the worse the detection. Thus, our proposed methods were satisfactory and are promising for assessing planting in a remote and accessible way. Full article
(This article belongs to the Special Issue Application of Image Processing in Agriculture)
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