Precision Farming and Control of Crop Production

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 4733

Special Issue Editors

Department of Engineering and Applied Sciences, University of Bergamo, Via Salvecchio 19, 24129 Bergamo, Italy
Interests: structural and infra-structural monitoring with new geomatic techniques (MEMS sensors, UAV platforms, remote sensing)
Special Issues, Collections and Topics in MDPI journals
CIRGEO Interdepartmental Research Center of Geomatics, Department of Land, Environment, Agriculture and Forestry, University of Padua, TESAF, 35122 Padova, PD, Italy
Interests: GNSS; UAV; terrestrial laser scanning; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Shibendu S. Ray
E-Mail Website
Co-Guest Editor
Mahalanobis National Crop Forecast Centre, Ministry of Agriculture, New Delhi, Delhi 110001, India
Interests: crop forecasting; drought assessment; crop insurance; precision farming

Special Issue Information

Dear Colleagues,

The industrial agri-food system that has been established over the last fifty years has led to the indiscriminate exploitation and irreversible deterioration of natural resources, incorrectly assumed to be unlimited and inexhaustible. The costs incurred in environmental and social terms as a result of intensive agriculture have been enormous, especially in terms of pollution, loss of biodiversity, soil fertility reduction, and marginal land abandonment, thus creating obvious sustainability issues. Nowadays, a possible response to the negative trend just described is represented by the development of technologies and the implementation of so-called precision agriculture (PA), whose birth dates back to the nineties in the United States, where it continues to have the widest spread and technical and technological evolution.

The development of the technology over the past decades has created platforms and sensors that are useful for acquiring more and more accurate data from the observation of the terrestrial surface for both geometry and radiometry. At the same time, scientific knowledge has allowed us to develop methods and techniques of data treatment for extracting information about the state of the terrestrial surface and natural resources. In this context, agriculture is one of the primary fields of application of Earth observation techniques.

Data from remote sensors can provide relevant information about the development and the conditions of the agricultural crops, thanks to the availability of multi- and iper-spectral sensors. Precision farming, born from a rational use of resources, is based on the availability of very detailed and spatially distributed information, relative to the conditions and the state of the crops in the field. Multi-optic and hyper-spectral sensors, as well as thermal sensors, measure the reflected and emitted radiation from surfaces using visible/infra-red wavelengths; therefore, it is possible to determine some of the characteristics of vegetated surfaces on the basis of their interactions with the incident solar radiation (part of the visible/infra-red near and medium electromagnetic spectrum) and/or on the basis of the emitted radiation (part of the infra-red electromagnetic thermal spectrum).

Data acquisition is not a trivial operation in agriculture, mainly because information is not directly usable if it is not gathered from appropriately calibrated sensors. Consequently, precision farming requires a cyclical process of data collection in a closed-chain control scheme for crop management and the evaluation of decisions, along with the peculiarity that a monitoring action cannot be limited to a single crop cycle, but spans several years to come, while retaining the memory of previous cycles. 

Prof. Maria Grazia D'Urso
Prof. Alberto Guarnieri
Dr. Shibendu S. Ray
Guest Editor

Manuscript Submission Information

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Keywords

  • crops
  • precision farming
  • growth index
  • calibration camera
  • multi-spectral
  • thermal images
  • platforms
  • sensors
  • wavelengths
  • electromagnetic spectrum
  • georeferencing
  • geomatic monitoring

Published Papers (2 papers)

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Research

19 pages, 9041 KiB  
Article
Determining the Effects of Nanonutrient Application in Cabbage (Brassica oleracea var. capitate L.) Using Spectrometry and Biomass Estimation with UAV
Agronomy 2022, 12(1), 81; https://doi.org/10.3390/agronomy12010081 - 30 Dec 2021
Cited by 4 | Viewed by 2211
Abstract
Geospatial technologies are presented as an alternative for the monitoring and control of crops, as demonstrated through the analysis of spectral responses (SR) of each species. In this study, it was intended to determine the effects of the application of nanonutrients (Zn and [...] Read more.
Geospatial technologies are presented as an alternative for the monitoring and control of crops, as demonstrated through the analysis of spectral responses (SR) of each species. In this study, it was intended to determine the effects of the application of nanonutrients (Zn and Mn) in cabbage (Brassica oleracea var. capitate L.) by analyzing the relationship between the vegetation indices (VI) NDVI, GNDVI, NGRDI, RVI, GVI, CCI RARSa and the content of chlorophyll (CC), from two trials established in the field and in the greenhouse, together with the calculation of dry biomass production in the field through the use of digital models and its further validation. The results indicated that for greenhouse experiments no significant differences were found between the VIs in the implemented treatments, rather for their phenological states. Whereas in the field assays it was evidenced that there were significant differences between the VIs for the treatments, as well as for the phenological states. The SR issued in the field allowed the evaluation of the behavior of the crop due to the application of nanonutrients, which did not occur in the greenhouse, in the same way. The SR also enabled the spectral characterization of the crop in its phenological states in the two trials. All this information was stored in a digital format, which allowed the creation of a spectral library which was published on a web server. The validation of the dry biomass allowed, by statistical analysis, the efficiency of the method used for its estimation to be confirmed. Full article
(This article belongs to the Special Issue Precision Farming and Control of Crop Production)
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20 pages, 7156 KiB  
Article
High-Throughput Root Network System Analysis for Low Phosphorus Tolerance in Maize at Seedling Stage
Agronomy 2021, 11(11), 2230; https://doi.org/10.3390/agronomy11112230 - 03 Nov 2021
Cited by 4 | Viewed by 1943
Abstract
The root system is the important organ of a plant, helping to anchor the plant and take up nutrients from the soil. The purpose of this investigation was to determine the magnitude of the root network system (RNS) through phenotypic variability in a [...] Read more.
The root system is the important organ of a plant, helping to anchor the plant and take up nutrients from the soil. The purpose of this investigation was to determine the magnitude of the root network system (RNS) through phenotypic variability in a broad range of maize inbred lines. The GiA Root software was used to identify root attributes from images. After germination, the inbred lines were grown hydroponically for 15 days in a high-lux plant growth room with low phosphorus (LP) and normal phosphorus (NP) treatments. Variance analysis revealed a large range of variability present among the inbred lines, with intermediate to high heritabilities ranging from 0.59 to 0.95 for all RNS traits, demonstrating uniformity through the experiments. The proportions of genetic variance ranged from 0.01–0.60 in different maize RNS traits. A strong positive linear relationship between best linear unbiased predictors (BLUPs) with estimated means was found for all the RNS traits. The Euclidean genetic distances between the studied inbred lines ranged from 0.61 to 29.33, showing a higher amount of diversity. More than 79% of the overall genetic variation was explained by the first three principal components, with high loadings from the measurements of network length (NWL), network surface area (NWSA), network perimeter (NWP), network area (NWA), the maximum number of roots (MANR), median number of roots (MENR), network volume (NWV), network convex area (NWCA), specific root length (SRL), network depth (NWD), number of connected components (NCC), and network width (NWW). The biplot of genotype by trait interaction exposed superior genotypes with a relatively high expression of favorable trait combinations. Some outstanding genotypes with higher values of most RNS traits were identified through MGIDI analysis. These lines may be convenient for enhancing LP tolerance in maize. Full article
(This article belongs to the Special Issue Precision Farming and Control of Crop Production)
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