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Agronomy
Special Issues
Application of Agriculture Digitization in Cropping Systems
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Application of Agriculture Digitization in Cropping Systems

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

Deadline for manuscript submissions: closed (25 September 2022) | Viewed by 4218

Special Issue Editor

Prof. Dr. Ole Green

E-Mail Website
Guest Editor
Department of AgroEcology, Aarhus University, Blichers Allé 20, Tjele, Danmark
Interests: technology and intelligent solutions for sustainable soil management; robotics and precision farming technologies; specifically within soil tillage optimization; crop establishment and plant nursing; farm management systems for tillage and crop growth optimization; route planning and operations optimization

Special Issue Information

Dear Colleagues,

The rapid adoption of new digital technologies and solutions across society has introduced several new possibilities in agriculture, and increased demands for digitalization across the food production industry, from primary production throughout the processing industry.

Innovations such as 5G, IOT, big data, cloud computing, and neural network have enabled fast growth, offering new solutions for farmers and growers for the digitalization of cropping systems based on new sensors, actuation systems, and decision-support systems.

Global pains within agriculture—such as uniform repetitive working conditions, increasing weed resistance problems and society’s demands for continued reduction in the use of pesticides, increasing demand for food and feed for a growing population, and a higher requirement for food safety, along with the continual cost optimization in the primary agricultural production—all contribute to the need for new solutions.

This Special Issue on the application of agriculture digitization in cropping systems aims to present novel research in the implementation and use of these new technologies for optimization and sustainable intensification of cropping systems, along with research that presents how value is created using these new digital solutions and technologies. The digitization of cropping systems will be an extremely important part of agricultural development over the next decade; by highlighting and promoting these new possibilities, we hope the agricultural domain will be able to share the new knowledge and experiences with similar high adoption rate.

Prof. Dr. Ole Green
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

  • decision support systems
  • farm management
  • digital farming
  • internet of things
  • 5G
  • big data
  • cloud computing
  • neural network

Published Papers (2 papers)

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Research

13 pages, 1687 KiB  
Article
Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil
by Eduardo Leonel Bottega, José Lucas Safanelli, Mojtaba Zeraatpisheh, Telmo Jorge Carneiro Amado, Daniel Marçal de Queiroz and Zanandra Boff de Oliveira
Agronomy 2022, 12(6), 1390; https://doi.org/10.3390/agronomy12061390 - 09 Jun 2022
Cited by 1 | Viewed by 1857
Abstract
Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable [...] Read more.
Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs according. Full article
(This article belongs to the Special Issue Application of Agriculture Digitization in Cropping Systems)
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24 pages, 20655 KiB  
Article
Novel Assessment of Region-Based CNNs for Detecting Monocot/Dicot Weeds in Dense Field Environments
by Nima Teimouri, Rasmus Nyholm Jørgensen and Ole Green
Agronomy 2022, 12(5), 1167; https://doi.org/10.3390/agronomy12051167 - 12 May 2022
Cited by 7 | Viewed by 1920
Abstract
Weeding operations represent an effective approach to increase crop yields. Reliable and precise weed detection is a prerequisite for achieving high-precision weed monitoring and control in precision agriculture. To develop an effective approach for detecting weeds within the red, green, and blue (RGB) [...] Read more.
Weeding operations represent an effective approach to increase crop yields. Reliable and precise weed detection is a prerequisite for achieving high-precision weed monitoring and control in precision agriculture. To develop an effective approach for detecting weeds within the red, green, and blue (RGB) images, two state-of-the-art object detection models, EfficientDet (coefficient 3) and YOLOv5m, were trained on more than 26,000 in situ labeled images with monocot/dicot classes recorded from more than 200 different fields in Denmark. The dataset was collected using a high velocity camera (HVCAM) equipped with a xenon ring flash that overrules the sunlight and minimize shadows, which enables the camera to record images with a horizontal velocity of over 50 km h-1. Software-wise, a novel image processing algorithm was developed and utilized to generate synthetic images for testing the model performance on some difficult occluded images with weeds that were properly generated using the proposed algorithm. Both deep-learning networks were trained on in-situ images and then evaluated on both synthetic and new unseen in-situ images to assess their performances. The obtained average precision (AP) of both EfficientDet and YOLOv5 models on 6625 synthetic images were 64.27% and 63.23%, respectively, for the monocot class and 45.96% and 37.11% for the dicot class. These results confirmed that both deep-learning networks could detect weeds with high performance. However, it is essential to verify both the model’s robustness on in-situ images in which there is heavy occlusion with a complicated background. Therefore, 1149 in-field images were recorded in 5 different fields in Denmark and then utilized to evaluate both proposed model’s robustness. In the next step, by running both models on 1149 in-situ images, the AP of monocot/dicot for EfficientDet and YOLOv5 models obtained 27.43%/42.91% and 30.70%/51.50%, respectively. Furthermore, this paper provides information regarding challenges of monocot/dicot weed detection by releasing 1149 in situ test images with their corresponding labels (RoboWeedMap) publicly to facilitate the research in the weed detection domain within the precision agriculture field. Full article
(This article belongs to the Special Issue Application of Agriculture Digitization in Cropping Systems)
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