Research

12 pages, 3890 KiB

Open AccessArticle

Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow

by George Deroco Martins, Laura Cristina Moura Xavier, Guilherme Pereira de Oliveira, Maria de Lourdes Bueno Trindade Gallo, Carlos Alberto Matias de Abreu Júnior, Bruno Sérgio Vieira, Douglas José Marques and Filipe Vieira da Silva

Agronomy 2023, 13(3), 808; https://doi.org/10.3390/agronomy13030808 - 10 Mar 2023

Viewed by 1173

Abstract

The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring [...] Read more.

The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R² to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R² to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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14 pages, 3144 KiB

Open AccessArticle

Drought and Waterlogging Status and Dominant Meteorological Factors Affecting Maize (Zea mays L.) in Different Growth and Development Stages in Northeast China

by Xiaowei Wang, Xiaoyu Li, Jiatong Gu, Wenqi Shi, Haigen Zhao, Chen Sun and Songcai You

Agronomy 2023, 13(2), 374; https://doi.org/10.3390/agronomy13020374 - 27 Jan 2023

Cited by 4 | Viewed by 1223

Abstract

Drought and floods affect the growth and yield of maize, affecting food security. Therefore, it is crucial to assess maize’s drought and waterlogging status in various growth stages. We used phenological and daily meteorological data and spatial analysis to identify the drought and [...] Read more.

Drought and floods affect the growth and yield of maize, affecting food security. Therefore, it is crucial to assess maize’s drought and waterlogging status in various growth stages. We used phenological and daily meteorological data and spatial analysis to identify the drought and waterlogging conditions of spring maize in Northeast China in eight growth stages. We calculated the crop water surplus/deficit index and used the national standard for maize drought and waterlogging. The results indicate a significant decreasing trend of effective precipitation in Northeast China. The maize’s water requirements changed during the growing period. The ranking of the daily water requirements of maize from high to low in the different growth stages was the flowering stage to the silking stage (6.9 mm/d), the tasseling stage to the flowering stage (6.1 mm/d), the jointing stage to the tasseling stage (4.9 mm/d), the seven-leaf stage to the jointing stage (3.4 mm/d), the silking stage to the harvesting stage (2.0 mm/d), the emergence stage to the three-leaf stage (1.4 mm/d), the three-leaf stage to the seven-leaf stage (1.3 mm/d), and the sowing stage to the emergence stage (1.2 mm/d). Drought occurred primarily in the early growth and development stage, and the most severe drought conditions were observed in the sowing to emergence stages and the emergence to the three-leaf stages in most areas in Northeast China. Waterlogging occurred predominantly in the flowering to the silking stages and the silking to the maturity stages in southeast Liaoning and parts of Jilin. Inner Mongolia had the lowest soil moisture conditions and was unsuitable for maize growth, followed by Heilongjiang, Jilin, and Liaoning. The dominant meteorological factors affecting the drought and waterlogging status of maize in different growth stages were precipitation and wind speed, followed by the minimum temperature, relative humidity, sunshine hours, and maximum temperature. The average temperature did not influence the drought and waterlogging status. The results provide a basis for selecting drought-resistant varieties and preventing waterlogging. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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16 pages, 20351 KiB

Open AccessEditor’s ChoiceArticle

A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

by Dorijan Radočaj, Ante Šiljeg, Ivan Plaščak, Ivan Marić and Mladen Jurišić

Agronomy 2023, 13(2), 362; https://doi.org/10.3390/agronomy13020362 - 26 Jan 2023

Cited by 3 | Viewed by 1314

Abstract

This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing [...] Read more.

This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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15 pages, 4224 KiB

Open AccessArticle

Mapping of Phenological Traits in Northeast China Maize (Zea mays L.)

by Xiaowei Wang, Xiaoyu Li, Jiatong Gu, Wenqi Shi, Haigen Zhao, Chen Sun and Songcai You

Agronomy 2022, 12(10), 2585; https://doi.org/10.3390/agronomy12102585 - 20 Oct 2022

Cited by 4 | Viewed by 1405

Abstract

Detailed traits are required for early warning and prediction of crop-related meteorological hazards. Currently, data sets describing maize phenological traits in Northeast China are few and incomplete, resulting in poor spatial interpolation results that do not accurately reflect the spatial distributions and temporal [...] Read more.

Detailed traits are required for early warning and prediction of crop-related meteorological hazards. Currently, data sets describing maize phenological traits in Northeast China are few and incomplete, resulting in poor spatial interpolation results that do not accurately reflect the spatial distributions and temporal development patterns of maize phenology in the region. In this study, a maize-phenology data set is produced containing nine phenological stages and phenological stage maps based on three sets of in situ maize-phenology data from three different sources. First, the relationship between each phenological stage and date of the previous stage, longitude, latitude, and altitude, is uncovered using a multiple stepwise regression method. Then, the spatial variation of each phenological stage using ArcGIS is explored. Finally, a maize phenological stage data set and a phenological stage atlas are established for the average state of 2010–2020 in Northeast China. The data set was validated using phenological data from agricultural weather stations run by the China Meteorological Administration. The validated data set can be used for various purposes, including real-time warning and prediction of maize-related meteorological hazards. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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24 pages, 10170 KiB

Open AccessArticle

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

by Francisco Javier López-Andreu, Zaida Hernández-Guillen, Jose Antonio Domínguez-Gómez, Marta Sánchez-Alcaraz, Juan Antonio Carrero-Rodrigo, Joaquin Francisco Atenza-Juárez, Juan Antonio López-Morales and Manuel Erena

Agronomy 2022, 12(7), 1674; https://doi.org/10.3390/agronomy12071674 - 14 Jul 2022

Viewed by 1684

Abstract

This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the [...] Read more.

This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the improvement of their overall biodiversity population. Two methodologies were initially defined, one based on remote sensing (BirdsEO) and the other on Machine Learning (BirdsML). Both use Sentinel-1 and Sentinel-2 data as a basis. BirdsEO encountered certain impediments caused by the land’s slope and the crop’s height. Finally, the methodology based on Machine Learning offered the best results. It evaluated the performance of up to 7 different Machine Learning classifiers, the most optimal being RandomForest. Fourteen different datasets were generated, and the results they offered were evaluated, the most optimal being the one with more than 150 features, including a time series of 8 elements with Sentinel-1, Sentinel-2 data and derived products, among others. The generated model provided values higher than 97% in metrics such as accuracy, recall and Area under the ROC Curve, and 95% in precision and recall. The methodology is transformed into a tool that continuously monitors 100% of the area requesting aid, continuously over time, which contributes positively to optimizing the use of administrative resources and a fairer distribution of CAP funds. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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14 pages, 5192 KiB

Open AccessArticle

Remote Sensing of Maize Plant Height at Different Growth Stages Using UAV-Based Digital Surface Models (DSM)

by Leon Hinrich Oehme, Alice-Jacqueline Reineke, Thea Mi Weiß, Tobias Würschum, Xiongkui He and Joachim Müller

Agronomy 2022, 12(4), 958; https://doi.org/10.3390/agronomy12040958 - 15 Apr 2022

Cited by 12 | Viewed by 2729

Abstract

Plant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and [...] Read more.

Plant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and time consuming. Therefore, we established digital surface models (DSM) based on RGB-images captured by an unmanned aerial vehicle (UAV) at five different dates throughout the growth period to rapidly estimate plant height of 400 maize genotypes. The UAV-based estimation of plant height (PH_UAV) was compared to the manual measurement from the ground to the highest leaf (PH_L), to the tip of the manually straightened highest leaf (PH_S) and, on the final date, to the top of the tassel (PH_T). The best results were obtained for estimating both PH_L (0.44 ≤ R² ≤ 0.51) and PH_S (0.50 ≤ R² ≤ 0.61) from 39 to 68 days after sowing (DAS). After calibration the mean absolute percentage error (MAPE) between PH_UAV and PH_S was in a range from 12.07% to 19.62%. It is recommended to apply UAV-based maize height estimation from 0.2 m average plant height to maturity before the plants start to senesce and change the leaf color. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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15 pages, 1508 KiB

Open AccessArticle

Long-Term Monitoring of Different Field Traffic Management Practices in Cereals Production with Support of Satellite Images and Yield Data in Context of Climate Change

by Vladimír Rataj, Jitka Kumhálová, Miroslav Macák, Marek Barát, Jana Galambošová, Jan Chyba and František Kumhála

Agronomy 2022, 12(1), 128; https://doi.org/10.3390/agronomy12010128 - 05 Jan 2022

Cited by 5 | Viewed by 1761

Abstract

Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the [...] Read more.

Cereals in Europe are mainly grown with intensive management. This often leads to the deterioration of the physical properties of the soil, especially increasing bulk density due to heavy machinery traffic, which causes excessive soil compaction. Controlled traffic farming (CTF) technology has the potential to address these issues, as it should be advantageous technology for growing cereals during climate change. The aim of this study was to compare the yield potential of CTF and standardly used random traffic farming (RTF) technology using yield maps obtained from combine harvester and satellite imagery as a remote sensing method. The experiment was performed on a 16-hectare experimental field with a CTF system established in 2009 (with conversion from a conventional (ploughing) to conservation tillage system). Yield was compared in years when small cereals were grown, a total of 7 years within a 13-year period (2009–2021). The results show that CTF technology was advantageous in dry years. Cereals grown in the years 2016, 2017 and 2019 had significantly higher yields under CTF technology. On the contrary, in years with higher precipitation, RTF technology had slightly better results—up to 4%. This confirms higher productivity when using CTF technology in times of climate change. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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13 pages, 2529 KiB

Open AccessArticle

Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry

by Marta García-Fernández, Enoc Sanz-Ablanedo, Dimas Pereira-Obaya and José Ramón Rodríguez-Pérez

Agronomy 2021, 11(12), 2489; https://doi.org/10.3390/agronomy11122489 - 08 Dec 2021

Cited by 10 | Viewed by 2950

Abstract

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing [...] Read more.

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R² was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R² = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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15 pages, 3103 KiB

Open AccessArticle

The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

by Dorijan Radočaj, Irena Jug, Vesna Vukadinović, Mladen Jurišić and Mateo Gašparović

Agronomy 2021, 11(12), 2430; https://doi.org/10.3390/agronomy11122430 - 29 Nov 2021

Cited by 17 | Viewed by 2401

Abstract

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy [...] Read more.

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P₂O₅) and potassium oxide (K₂O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R² in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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18 pages, 3287 KiB

Open AccessArticle

Detection of Crop Hail Damage with a Machine Learning Algorithm Using Time Series of Remote Sensing Data

by Leandro Sosa, Ana Justel and Íñigo Molina

Agronomy 2021, 11(10), 2078; https://doi.org/10.3390/agronomy11102078 - 18 Oct 2021

Cited by 11 | Viewed by 3548

Abstract

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This [...] Read more.

Hailstorms usually result in total crop loss. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss. Assessment accuracy depends largely on in situ detection of homogeneous damage sectors within the field, using visual techniques. This paper presents an algorithm for the automatic detection of homogeneous hail damage through the application of unsupervised machine learning techniques to vegetation indices calculated from remote sensing data. Five microwave and five spectral indices were evaluated before and after a hailstorm in zones with different degrees of damage. Dual Polarization SAR Vegetation Index and Normalized Pigment Chlorophyll Ratio Index were the most sensitive to hail-induced changes. The time series and rates of change of these indices were used as input variables in the K-means method for clustering pixels into homogeneous damage zones. Validation of the algorithm with data from 91 soybean, wheat, and corn plots showed that in 87.01% of cases there was significant evidence of differences in average damage between zones determined by the algorithm within the plot. Thus, the algorithm presented in this paper allowed efficient detection of homogeneous hail damage zones, which is expected to improve accuracy and transparency in the characterization of hailstorm events. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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15 pages, 2376 KiB

Open AccessArticle

Assessment of the SASI Spectral Shape Index Time Series for Mapping Rice Ecosystems in the Mediterranean Region

by Lucía Tornos, José Antonio Domínguez, Maria C. Moyano, Laura Recuero, Víctor Cicuéndez, María Jesús García-García and Alicia Palacios-Orueta

Agronomy 2021, 11(7), 1365; https://doi.org/10.3390/agronomy11071365 - 05 Jul 2021

Cited by 4 | Viewed by 2081

Abstract

There is a growing need to map rice ecosystems and to develop methods for monitoring rice distribution in order to account for rapid land use changes worldwide. In this study, we evaluated a methodology based on Vegetation Indices time series derived from an [...] Read more.

There is a growing need to map rice ecosystems and to develop methods for monitoring rice distribution in order to account for rapid land use changes worldwide. In this study, we evaluated a methodology based on Vegetation Indices time series derived from an 8-day MODIS composite to identify rice fields and develop rice maps that can be timely updated in the long term. We have assessed the potential of the Spectral Shape Index time series and compared its performance with the Normalized Difference Vegetation Index in two coastal locations and in an inland location in the Mediterranean Region for 2012. A profile similarity comparison method, the Spectral Angle Mapper, was accomplished between the reference rice annual profile and the annual profiles of both indices in a pixel basis in order to determine rice pixels. The resultant maps were validated with rice masks, where available, or ortophotos and crop surface statistics where not. The results obtained demonstrated the potential of both indices to provide accurate rice maps when applied together with spectral matching techniques. The overall accuracy was 92.8%, 98.1% and 90.1% for the Spectral Shape Index and 92.4%, 77.24% and 82.8% for the Normalized Difference Vegetation Index in each location. The excellent performance of the Spectral Shape Index in the three locations highlighted the importance of exploring angular indices to improve the identification of land cover dynamics. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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16 pages, 2277 KiB

Open AccessArticle

Monitoring of Khorasan (Triticum turgidum ssp. Turanicum) and Modern Kabot Spring Wheat (Triticum aestivum) Varieties by UAV and Sensor Technologies under Different Soil Tillage

by Kristýna Balážová, Jan Chyba, Jitka Kumhálová, Jiří Mašek and Stanislav Petrásek

Agronomy 2021, 11(7), 1348; https://doi.org/10.3390/agronomy11071348 - 30 Jun 2021

Cited by 4 | Viewed by 2496

Abstract

Khorasan wheat (Triticum turgidum ssp. turanicum (Jakubz.)) is an ancient tetraploid spring wheat variety originating from northeast parts of Central Asia. This variety can serve as a full-fledged alternative to modern wheat but has a lower yield than modern varieties. [...] Read more.

Khorasan wheat (Triticum turgidum ssp. turanicum (Jakubz.)) is an ancient tetraploid spring wheat variety originating from northeast parts of Central Asia. This variety can serve as a full-fledged alternative to modern wheat but has a lower yield than modern varieties. It is commonly known that wheat growth is influenced by soil tillage technology (among other things). However, it is not known how soil tillage technology affects ancient varieties. Therefore, the main objective of this study was to evaluate the influence of different soil tillage technologies on the growth of the ancient Khorasan wheat variety in comparison to the modern Kabot spring wheat (Triticum aestivum) variety. The trial was arranged in six small plots, one half of which was sown by the Khorasan wheat variety and the other half of which was sown by the Kabot wheat variety. Three soil tillage methods were used for each cultivar: conventional tillage (CT) (20–25 cm), minimum tillage (MTC) with a coulter cultivator (15 cm), and minimization tillage (MTD) with a disc cultivator (12 cm). The soil surface of all of the variants were leveled after tillage (harrows & levelling bars). An unmanned aerial vehicle with multispectral and thermal cameras was used to monitor growth during the vegetation season. The flight missions were supplemented by measurements using the GreenSeeker hand-held sensor and plant and soil analysis. The results showed that the Khorasan ancient wheat was better suited the conditions of conventional tillage, with low values of bulk density and highvalues of total soil porosity, which generally increased the nutritional value of the yield in this experimental plot. At the same time, it was found that this ancient wheat does not deplete the soil. The results also showed that the trend of developmental growing curves derived from different sensors was very similar regardless of measurement method. The sensors used in this study can be good indicators of micronutrient content in the plant as well as in the grains. A low-cost RGB camera can provide relevant results, especially in cases where equipment that is more accurate is not available. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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18 pages, 3948 KiB

Open AccessEditor’s ChoiceArticle

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

by Veena Shashikant, Abdul Rashid Mohamed Shariff, Aimrun Wayayok, Md Rowshon Kamal, Yang Ping Lee and Wataru Takeuchi

Agronomy 2021, 11(6), 1243; https://doi.org/10.3390/agronomy11061243 - 19 Jun 2021

Cited by 26 | Viewed by 4128

Abstract

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought [...] Read more.

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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18 pages, 8416 KiB

Open AccessArticle

Monitoring Errors of Semi-Mechanized Coffee Planting by Remotely Piloted Aircraft

by Lucas Santos Santana, Gabriel Araújo e Silva Ferraz, João Paulo Barreto Cunha, Mozarte Santos Santana, Rafael de Oliveira Faria, Diego Bedin Marin, Giuseppe Rossi, Leonardo Conti, Marco Vieri and Daniele Sarri

Agronomy 2021, 11(6), 1224; https://doi.org/10.3390/agronomy11061224 - 16 Jun 2021

Cited by 13 | Viewed by 2542

Abstract

Mechanized operations on terrain slopes can still lead to considerable errors in the alignment and distribution of plants. Knowing slope interference in semi-mechanized planting quality can contribute to precision improvement in decision making, mainly in regions with high slope. This study evaluates the [...] Read more.

Mechanized operations on terrain slopes can still lead to considerable errors in the alignment and distribution of plants. Knowing slope interference in semi-mechanized planting quality can contribute to precision improvement in decision making, mainly in regions with high slope. This study evaluates the quality of semi-mechanized coffee planting in different land slopes using a remotely piloted aircraft (RPA) and statistical process control (SPC). In a commercial coffee plantation, aerial images were collected by a remotely piloted aircraft (RPA) and subsequently transformed into a digital elevation model (DEM) and a slope map. Slope data were subjected to variance analysis and statistical process control (SPC). Dependent variables analyzed were variations in distance between planting lines and between plants in line. The distribution of plants on all the slopes evaluated was below expected; the most impacted was the slope between 20–25%, implementing 7.8% fewer plants than projected. Inferences about the spacing between plants in the planting row showed that in slopes between 30–40%, the spacing was 0.53 m and between 0 and 15% was 0.55 m. This denotes the compensation of the speed of the operation on different slopes. The spacing between the planting lines had unusual variations on steep slopes. The SCP quality graphics are of lower quality in operations between 30–40%, as they have an average spacing of 3.65 m and discrepant points in the graphics. Spacing variations were observed in all slopes as shown in the SCP charts, and possible causes and implications for future management were discussed, contributing to improvements in the culture installation stage. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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15 pages, 7937 KiB

Open AccessArticle

QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

by Lia Duarte, Ana Cláudia Teodoro, Joaquim J. Sousa and Luís Pádua

Agronomy 2021, 11(5), 952; https://doi.org/10.3390/agronomy11050952 - 11 May 2021

Cited by 15 | Viewed by 3640

Abstract

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case [...] Read more.

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study

by Francisco Javier López-Andreu, Manuel Erena, Jose Antonio Dominguez-Gómez and Juan Antonio López-Morales

Agronomy 2021, 11(4), 621; https://doi.org/10.3390/agronomy11040621 - 25 Mar 2021

Cited by 14 | Viewed by 4228

Abstract

The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus [...] Read more.

The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus Programme. The Sentinel-1 (radar range) and Sentinel-2 (optical range) satellites have been designed for monitoring agricultural problems based on the characteristics they provide. The data provided by the Sentinel 2 missions, together with the emergence of different scientific disciplines in artificial intelligence —especially machine learning— offer the perfect basis for identifying and classifying any crop and its phenological state. Our research is based on developing and evaluating a pixel-based supervised classification scheme to produce accurate rice crop mapping in a smallholder agricultural zone in Calasparra, Murcia, Spain. Several models are considered to obtain the most suitable model for each element of the time series used; pixel-based classification is performed and finished with a statistical treatment. The highly accurate results obtained, especially across the most significant vegetative development dates, indicate the benefits of using Sentinel-2 data combined with Machine Learning techniques to identify rice crops. It should be noted that it was possible to locate rice crop areas with an overall accuracy of 94% and standard deviation of 1%, which could be increased to 96% (±1%) if we focus on the months of the crop’s highest development state. Thanks to the proposed methodology, the on-site inspections carried out, 5% of the files, have been replaced by remote sensing evaluations of 100% of the analyzed season files. Besides, by adjusting the model input data, it is possible to detect unproductive or abandoned plots. Full article

(This article belongs to the Special Issue Geoinformatics Application in Agriculture)

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