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New Technologies and Spatiotemporal Approaches in Precision Agriculture

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (20 June 2021) | Viewed by 66804

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Prof. Dr. Dionissios Kalivas

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Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Interests: land evaluation; site specific crop management; digital farming; GIS; remote sensing; spatial analysis; soil information systems
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Dear Colleagues,

The ever-increasing nutritional demands have led to the use of large quantities of various inputs to satisfy the requirement of increased agricultural production. At the same time, the stabilization and improvement of the quality of crop and food production is a major challenge facing today’s agriculture.

The effects of preharvest factors on the ultimate quantity and quality of harvested products are often overlooked and underestimated, although a wide spectrum of preharvest factors, including environmental conditions and field management practices, directly or indirectly impact field crops’ yield characteristics. In particular, seasonal climatic conditions, soil fertility, variety selection, fertilization, irrigation, pest control, weed management, and harvest time play a crucial role in determining yield quantities and postharvest quality attributes (such as color, flavor, texture, and nutritional value of the harvested product) and, subsequently, consumers’ decision to purchase the product in the marketplace. Modern precision agriculture has provided substantial solutions that address the spatial and temporal dimensions of field management practices.

This Special Issue focuses on the role of geospatial digital technologies (GIS, RS-UAV, GPS) and sensors (image, climate, soil) in the capture, monitoring, and spatiotemporal analysis of data from preharvest factors toward better agricultural decision making in terms of space and time, with a major emphasis on the best agronomic practices for finally obtaining products with high and stable quality. For this Special Issue, we welcome all types of articles, including original research articles and reviews.

Prof. Dr. Dionissios Kalivas
Guest Editor

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Keywords

precision agriculture
GIS and remote sensing – UAV in precision agriculture
geodatabases
farm management information systems – FMIS
spatiotemporal data analysis
geostatistics
site specific crop management – SSCM
farm management zones
product quality
agronomic practices

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Published Papers (9 papers)

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Research

Jump to: Review

15 pages, 5292 KiB

Open AccessArticle

Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation

by Nikolas Perros, Dionissios Kalivas and Rigas Giovos

Agriculture 2021, 11(9), 809; https://doi.org/10.3390/agriculture11090809 - 26 Aug 2021

Cited by 11 | Viewed by 3278

Abstract

In this study, a spatial analysis of agronomic and remote sensing data is carried out to derive accurate rice crop yield estimation. The variability of a series of vegetation indices (VIs) was calculated from remote sensing data obtained via a commercial UAS platform (e-Bee) at four dates (per stage of development), and the development of estimation models was conducted. The study area is located in the region of Chalastra (municipality of Thessaloniki, North Greece) and the primary data were obtained during the 2016 growing season. These data include ultra-high resolution remote sensing multispectral images of 18 plots totaling 58 hectares of Ronaldo and Gladio rice crop varieties, 97 sample point data related to yield, and many other pieces of information recorded in the producer’s field log. Ten simple and compound VIs were calculated, and the evolution of their values during the growing season as well as their comparative correlation were studied. A study of the usability of each VI was conducted for the different phenological stages of the cultivation and the variance of VIs and yield; the more correlated VIs were identified. Furthermore, three types of multitemporal VI were calculated from combinations of VIs from different dates, and their contribution to improving yield prediction was studied. As Ronaldo is a Japonica type of rice variety and Gladio is Indica type, they behave differently in terms of maturation time (Gladio is approximately 20 days earlier) and the value of every VI is affected by changes in plant physiology and phenology. These differences between the two varieties are reflected in the multitemporal study of the single-date VIs but also in the study of the values of the multitemporal VIs. In conclusion, Ronaldo’s yield is strongly dependent on multitemporal NDVI (VI_{6th July} + VI_{30 Aug}, R² = 0.76), while Gladio’s yield is strongly dependent on single-date NDVI (6 July, R² = 0.88). The compound VIs RERDVI and MCARI1 have the highest yield prediction (R² = 0.77) for Ronaldo (VI_{6th July} + VI_{30 Aug}) and Gladio (R² = 0.95) when calculated in the booting stage, respectively. For the Ronaldo variety, the examination of the multitemporal VIs increases yield prediction accuracy, while in the case of the Gladio variety the opposite is observed. The capabilities of multitemporal VIs in yield estimation by combining UAVs with more flights during the different growth stages can improve management and the cultivation practices. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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20 pages, 47007 KiB

Open AccessArticle

Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region

by Dimitrios Tassopoulos, Dionissios Kalivas, Rigas Giovos, Nestor Lougkos and Anastasia Priovolou

Agriculture 2021, 11(8), 785; https://doi.org/10.3390/agriculture11080785 - 17 Aug 2021

Cited by 10 | Viewed by 3395

Abstract

Remote sensing satellite platforms provide accurate temporal and spatial information useful in viticulture with an increasing interest in their use. This study aims to identify the possibilities of freely available and with frequent revisit time Sentinel-2 satellites, to monitor vine growth at regional scale on a vine-growing Protected Designation of Origin (PDO) zone during the growing season of the year 2019. This study aims to: (i) investigate through several Vegetation Indices (VIs) the vine growth differences across the zone and relations with topographic parameters; (ii) identify VIs that best recognize differences on subzones of different climatic conditions; (iii) explore the effectiveness of the Sentinel-2 data monitoring management applications. A total of 27 vineyards were selected for field and satellite data collection. Several VIs have been calculated per vineyard from a 20-date time series dataset. VIs showed high negative correlation with topographic parameter of elevation on the flowering stage. The analysis of variance between the VIs of the subzones showed that these regions have statistically significant differences, that most VIs can expose on the flowering and harvest stage, and only Normalized Difference Vegetation Index (NDVI) and VIs using Red-Edge bands during the veraison period. Sentinel-2 data show great effectiveness on monitoring management applications (tillage and trimming). Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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17 pages, 3394 KiB

Open AccessArticle

Evaluation and Experiment of Flight Parameter Quality of the Plant Protection UAV Based on Laser Tracker

by Xin Huang, Xiaoya Dong, Jing Ma, Kuan Liu, Shibbir Ahmed, Jinlong Lin, Fiaz Ahmad and Baijing Qiu

Agriculture 2021, 11(7), 628; https://doi.org/10.3390/agriculture11070628 - 5 Jul 2021

Cited by 4 | Viewed by 2708

Abstract

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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17 pages, 8164 KiB

Open AccessArticle

Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery

by Rachane Malinee, Dimitris Stratoulias and Narissara Nuthammachot

Agriculture 2021, 11(3), 251; https://doi.org/10.3390/agriculture11030251 - 16 Mar 2021

Cited by 10 | Viewed by 5611

Abstract

Oil palm (Elaeis guineensis) trees are an important contributor of recent economic development in Southeast Asia. The high product yield, and the consequent high profitability, has led to a widespread expansion of plantations in the greater region. However, oil palms are susceptible to diseases that can have a detrimental effect. In this study we use hyper- and multi-spectral remote sensing to detect diseased oil palm trees in Krabi province, Thailand. Proximate spectroscopic measurements were used to identify and discern differences in leaf spectral radiance; the results indicate a relatively higher radiance in visible and near-infrared for the healthy leaves in comparison to the diseased. From a total of 113 samples for which the geolocation and the hyperspectral radiance were recorded, 59 and 54 samples were healthy and diseased oil palm trees, respectively. Moreover, a WorldView-2 satellite image was used to investigate the usability of traditional vegetation indices and subsequently detecting diseased oil palm trees. The results show that the overall maximum likelihood classification accuracy is 85.98%, the Kappa coefficient 0.71 and the producer’s accuracy for healthy and diseased oil palm trees 83.33 and 78.95, respectively. We conclude that high spatial and spectral resolutions can play a vital role in monitoring diseases in oil palm plantations. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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13 pages, 1550 KiB

Open AccessArticle

Research on Measurement Method of Leaf Length and Width Based on Point Cloud

by Yawei Wang, Yifei Chen, Xiangnan Zhang and Wenwen Gong

Agriculture 2021, 11(1), 63; https://doi.org/10.3390/agriculture11010063 - 13 Jan 2021

Cited by 5 | Viewed by 5980

Abstract

Leaf is an important organ for photosynthesis and transpiration associated with the plants’ growth. Through the study of leaf phenotype, it the physiological characteristics produced by the interaction of the morphological parameters with the environment can be understood. In order to realize the assessment of the spatial morphology of leaves, a method based on three-dimensional stereo vision was introduced to extract the shape information, including the length and width of the leaves. Firstly, a depth sensor was used to collect the point cloud of plant leaves. Then, the leaf coordinate system was adjusted by principal component analysis to extract the region of interest; and compared with a cross-sectional method, the geodesic distance method, we proposed a method based on the cutting plane to obtain the intersecting line of the three-dimensional leaf model. Eggplant leaves were used to compare the accuracy of these methods in the measurement of a single leaf. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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24 pages, 19062 KiB

Open AccessArticle

Predicting Phosphorus and Potato Yield Using Active and Passive Sensors

by Ahmed Jasim, Ahmed Zaeen, Lakesh K. Sharma, Sukhwinder K. Bali, Chunzeng Wang, Aaron Buzza and Andrei Alyokhin

Agriculture 2020, 10(11), 564; https://doi.org/10.3390/agriculture10110564 - 20 Nov 2020

Cited by 12 | Viewed by 3983

Abstract

Applications of remote sensing are important in improving potato production through the broader adoption of precision agriculture. This technology could be useful in decreasing the potential contamination of soil and water due to the over-fertilization of agriculture crops. The objective of this study was to assess the utility of active sensors (Crop Circle™, Holland Scientific, Inc., Lincoln, NE, USA and GreenSeeker™, Trimble Navigation Limited, Sunnyvale, CA, USA) and passive sensors (multispectral imaging with Unmanned Arial Vehicles (UAVs)) to predict total potato yield and phosphorus (P) uptake. The experimental design was a randomized complete block with four replications and six P treatments, ranging from 0 to 280 kg P ha⁻¹, as triple superphosphate (46% P₂O₅). Vegetation indices (VIs) and plant pigment levels were calculated at various time points during the potato growth cycle, correlated with total potato yields and P uptake by the stepwise fitting of multiple linear regression models. Data generated by Crop Circle™ and GreenSeeker™ had a low predictive value of potato yields, especially early in the season. Crop Circle™ performed better than GreenSeeker™ in predicting plant P uptake. In contrast, the passive sensor data provided good estimates of total yields early in the season but had a poor correlation with P uptake. The combined use of active and passive sensors presents an opportunity for better P management in potatoes. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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Review

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36 pages, 51244 KiB

Open AccessEditor’s ChoiceReview

A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland

by Darren J. Murphy, Michael D. Murphy, Bernadette O’Brien and Michael O’Donovan

Agriculture 2021, 11(7), 600; https://doi.org/10.3390/agriculture11070600 - 28 Jun 2021

Cited by 22 | Viewed by 6849

Abstract

The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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20 pages, 21304 KiB

Open AccessEditor’s ChoiceReview

Remote Sensing Vegetation Indices in Viticulture: A Critical Review

by Rigas Giovos, Dimitrios Tassopoulos, Dionissios Kalivas, Nestor Lougkos and Anastasia Priovolou

Agriculture 2021, 11(5), 457; https://doi.org/10.3390/agriculture11050457 - 18 May 2021

Cited by 97 | Viewed by 16281

Abstract

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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26 pages, 4574 KiB

Open AccessEditor’s ChoiceReview

A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture

by Mohammad Fatin Fatihur Rahman, Shurui Fan, Yan Zhang and Lei Chen

Agriculture 2021, 11(1), 22; https://doi.org/10.3390/agriculture11010022 - 1 Jan 2021

Cited by 88 | Viewed by 17067

Abstract

Presently in agriculture, there is much ample scope for drone and UAS (Unmanned Aircraft System) development. Because of their low cost and small size, these devices have the ability to help many developing countries with economic prosperity. The entire aggregation of financial investments in the agricultural area has increased appreciably in recent years. Sooth to say, agriculture remains a massive part of the world’s commercial growth, and due to some complications, the agriculture fields withstand massive losses. Pets and destructive insects seem to be the primary reasons for certain degenerative diseases. It minimizes the potential productivity of the crops. For increasing the quality of the plants, fertilizers and pesticides are appropriately applied. Using UAVs (Unmanned Aerial Vehicles) for spraying pesticides and fertilizing materials is an exuberant contraption. It adequately reduces the rate of health dilemma and the number of workers, which is quite an impressive landmark. Willing producers are also adopting UAVs in agriculture to soil and field analysis, seed sowing, lessen the time and costs correlated with crop scouting, and field mapping. It is rapid, and it can sensibly diminish a farmer’s workload, which is significantly a part of the agricultural revolution. This article aims to proportionally represent the concept of agricultural purposed UAV clear to the neophytes. First, this paper outlines the harmonic framework of the agricultural UAV, and then it abundantly illustrates the methods and materials. Finally, the article portrays the outcome. Full article

(This article belongs to the Special Issue New Technologies and Spatiotemporal Approaches in Precision Agriculture)

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