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Agronomy
Special Issues
Research Advances in Digital Technologies to Improve Agricultural Productivity
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Research Advances in Digital Technologies to Improve Agricultural Productivity

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

Deadline for manuscript submissions: closed (2 December 2022) | Viewed by 7486

Special Issue Editor

Dr. Rouverson Pereira Silva

E-Mail Website
Guest Editor
Department of Engineering and Mathematical Sciences, School of Agricultural and Veterinarian Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo 14884-900, Brazil
Interests: digital agriculture; smart harvest; agricultural mechanization; precision agriculture

Special Issue Information

Dear Colleagues,

Digital agriculture has developed quickly, and its applications are essential to meet the high demand for food production in a sustainable way, seeking to meet the United Nations Sustainable Development Goals. Digital technologies involve techniques associated with the organization and representation of information, mathematical and statistical modeling, sensing, and Artificial Intelligence, which allow, among other aspects, the traceability of products and processes and improvement of agricultural productivity. With this Special Issue of Agronomy, we seek integrative studies that demonstrate the progress of research that makes it possible to increase the productivity of agricultural crops, as well as reviews that offer original perspectives on the use of remote, aerial, and proximal sensing, artificial learning techniques, and/or about the use of robots in agriculture. In addition, we encourage contributions that present original studies focused on predicting productivity and maturity of crops under field conditions, the management of biotic and abiotic factors that affect productivity in the field, and use of agricultural robots to perform selective harvesting, spraying, and other agricultural operations.

Dr. Rouverson Pereira Silva
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital agriculture
  • Artificial Intelligence
  • remote sensing

Published Papers (3 papers)

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Research

14 pages, 2404 KiB  
Article
Identifying Nematode Damage on Soybean through Remote Sensing and Machine Learning Techniques
by Letícia Bernabé Santos, Leonardo Mendes Bastos, Mailson Freire de Oliveira, Pedro Luiz Martins Soares, Ignacio Antonio Ciampitti and Rouverson Pereira da Silva
Agronomy 2022, 12(10), 2404; https://doi.org/10.3390/agronomy12102404 - 05 Oct 2022
Cited by 5 | Viewed by 2218
Abstract
Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) [...] Read more.
Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants. Full article
(This article belongs to the Special Issue Research Advances in Digital Technologies to Improve Agricultural Productivity)
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13 pages, 3744 KiB  
Article
UAV Multispectral Data: A Reliable Approach for Managing Phosphate-Solubilizing Bacteria in Common Bean
by Antonia Erica Santos de Souza, Marcelo Rodrigues Barbosa Júnior, Bruno Rafael de Almeida Moreira, Rouverson Pereira da Silva and Leandro Borges Lemos
Agronomy 2022, 12(10), 2284; https://doi.org/10.3390/agronomy12102284 - 23 Sep 2022
Cited by 3 | Viewed by 1744
Abstract
Remote sensing can offer stakeholders opportunities to make precise and accurate decisions on agricultural activities. For instance, farmers can exploit aircraft systems to acquire survey-level, high-resolution imagery data for crop and soil management. Therefore, the objective of this study was to analyze whether [...] Read more.
Remote sensing can offer stakeholders opportunities to make precise and accurate decisions on agricultural activities. For instance, farmers can exploit aircraft systems to acquire survey-level, high-resolution imagery data for crop and soil management. Therefore, the objective of this study was to analyze whether an unmanned aerial vehicle (UAV) allows for the assessment and monitoring of biofertilization of the common bean upon vegetation indices (VIs). The biological treatment of the legume crop included its inoculation with phosphate-solubilizing bacteria (PSB), namely Bacillus subtilis and B. megaterium. Indicators of photosynthetic performance, such as chlorophylls (a and b) and carotenoids, were measured from actively growing leaves to determine effectiveness. In addition, images were acquired in the field, both spatially and temporally, to establish functional relationships between biometric and computational features. Microorganisms manifested as growth-promoting agents to the crop as they significantly increased its quantities of light-harvesting pigments. VIs allowed for predicting their impact on photosynthetic performance, making them on-site markers of PSB. Therefore, this research can provide insights into the remote, non-destructive mapping of spectral changes in the common bean upon the application of PSB. Imagery data from UAV would enable producers to generate information on the crop to intervene in the field at the right time and place for improved utilization of biofertilizers. Full article
(This article belongs to the Special Issue Research Advances in Digital Technologies to Improve Agricultural Productivity)
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11 pages, 1619 KiB  
Article
Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning
by Romário Porto de Oliveira, Marcelo Rodrigues Barbosa Júnior, Antônio Alves Pinto, Jean Lucas Pereira Oliveira, Cristiano Zerbato and Carlos Eduardo Angeli Furlani
Agronomy 2022, 12(9), 1992; https://doi.org/10.3390/agronomy12091992 - 23 Aug 2022
Cited by 11 | Viewed by 2907
Abstract
Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data [...] Read more.
Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters. Full article
(This article belongs to the Special Issue Research Advances in Digital Technologies to Improve Agricultural Productivity)
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