Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

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Variable-rate application technologies in precision agriculture

Precision agriculture for sustainability - Burleigh Dodds Series in Agricultural Science ◽

10.19103/as.2017.0032.08 ◽

2018 ◽

pp. 171-193

Author(s):

Kenneth A. Sudduth ◽

◽

Aaron J. Franzen ◽

Heping Zhu ◽

Scott T. Drummond ◽

...

Keyword(s):

Precision Agriculture ◽

Variable Rate ◽

Variable Rate Application

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Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers' historical paddock yields and normalised difference vegetation index

Crop and Pasture Science ◽

10.1071/cp08347 ◽

2009 ◽

Vol 60 (9) ◽

pp. 844 ◽

Cited By ~ 17

Author(s):

P. D. Fisher ◽

M. Abuzar ◽

M. A. Rab ◽

F. Best ◽

S. Chandra

Keyword(s):

Linear Relationship ◽

Precision Agriculture ◽

Vegetation Index ◽

Yield Variability ◽

Variable Rate ◽

Management Options ◽

Yield Data ◽

Alternative Management ◽

Financial Benefits ◽

Normalised Difference Vegetation Index

Despite considerable interest by Australian farmers in precision agriculture (PA), its uptake has been low. Analysis of the possible financial benefits of alternative management options that are based on the underlying patterns of observed spatial and temporal yield variability in a paddock could increase farmer confidence in adopting PA. The cost and difficulty in collecting harvester yield maps have meant that spatial yield data are generally not available in Australia. This study proposes a simple, economical and easy to use approach to generate simulated yield maps by using paddock-specific relationships between satellite normalised difference vegetation index (NDVI) and the farmer’s average paddock yield records. The concept behind the approach is illustrated using a limited dataset. For each of 12 paddocks in a property where a farmer’s paddock-level yield data were available for 3–5 years, the paddock-level yields showed a close to linear relationship with paddock-level NDVI across seasons. This estimated linear relationship for each paddock was used to simulate mean yields for the paddock at the subpaddock level at which NDVI data were available. For one paddock of 167 ha, for which 4 years of harvester yield data and 6 years of NDVI data were available, the map of simulated mean yield was compared with the map of harvester mean yield. The difference between the two maps, expressed as percentage deviation from the observed mean yield, was <20% for 63% of the paddock and <40% for 78% of the paddock area. For 3 seasons when there were both harvester yield data and NDVI data, the individual season simulated yields were within 30% of the observed yields for over 70% of the paddock area in 2 of the seasons, which is comparable with spatial crop modelling results reported elsewhere. For the third season, simulated yields were within 30% of the observed yield in only 22% of the paddock, but poor seasonal conditions meant that 40% of the paddock yielded <100 kg/ha. To illustrate the type of financial analysis of alternative management options that could be undertaken using the simulated yield data, a simple economic analysis comparing uniform v. variable rate nitrogen fertiliser is reported. This indicated that the benefits of using variable rate technology varied considerably between paddocks, depending on the degree of spatial yield variability. The proposed simulated yield mapping requires greater validation with larger datasets and a wider range of sites, but potentially offers growers and land managers a rapid and cost-effective tool for the initial estimation of subpaddock yield variability. Such maps could provide growers with the information necessary to carry out on-farm testing of the potential benefits of using variable applications of agronomic inputs, and to evaluate the financial benefits of greater investment in PA technology.

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Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat

Agriculture ◽

10.3390/agriculture11111104 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1104

Author(s):

Mohammad Rokhafrouz ◽

Hooman Latifi ◽

Ali A. Abkar ◽

Tomasz Wojciechowski ◽

Mirosław Czechlowski ◽

...

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Statistical Tests ◽

Unsupervised Clustering ◽

Data Availability ◽

Agricultural Drought ◽

Rank Test ◽

Variable Rate ◽

Management Zones ◽

Variable Rate Application

Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.

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Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Sensors ◽

10.3390/s21082861 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2861

Author(s):

Josephine Bukowiecki ◽

Till Rose ◽

Henning Kage

Keyword(s):

Precision Agriculture ◽

Near Infrared ◽

Absolute Error ◽

Yield Data ◽

Area Index ◽

Crop Monitoring ◽

Red Edge ◽

Using Data ◽

Simple Ratio ◽

Sentinel 2

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

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Variable Rate Irrigation (VRI)

Manuali – Scienze Tecnologiche - SPARKLE - Entrepreneurship for Sustainable Precision Agriculture ◽

10.36253/978-88-5518-044-3.22 ◽

2020 ◽

pp. 22

Author(s):

João Coimbra ◽

José Rafael Marques da Silva ◽

Manuela Correia

Keyword(s):

Precision Agriculture ◽

Variable Rate ◽

Irrigation Technology ◽

Variable Rate Application ◽

Variable Rate Irrigation

Many types of technology are used in variable rate application for Precision Agriculture. In this case, we are talking about Variable Rate Irrigation technology. Materials for this topic include a presentation and a text, that are complementary.

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Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data

Remote Sensing ◽

10.3390/rs11151745 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1745 ◽

Cited By ~ 11

Author(s):

Gómez ◽

Salvador ◽

Sanz ◽

Casanova

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Precision Agriculture ◽

Input Data ◽

Spatial Information ◽

Potato Yield ◽

Machine Learning Algorithms ◽

Support Vector ◽

Potato Yields ◽

Sentinel 2

Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.

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Variable Rate Application of Herbicides for Weed Management in Pre- and Postemergence

Pests, Weeds and Diseases in Agricultural Crop and Animal Husbandry Production ◽

10.5772/intechopen.93558 ◽

2020 ◽

Author(s):

Alessandro da Costa Lima ◽

Kassio Ferreira Mendes

Keyword(s):

Environmental Impact ◽

Precision Agriculture ◽

Weed Management ◽

Crop Productivity ◽

Variable Rate ◽

Chemical Management ◽

Variable Rate Application ◽

Skilled Workforce ◽

Investment Capacity

With the advent of precision agriculture, it was possible to integrate several technologies to develop the variable rate application (VRA). The use of VRA allows savings in the use of herbicides, better weed control, lower environmental impact and, indirectly, increased crop productivity. There are VRA techniques based on maps and sensors for herbicide application in preemergence (PRE) and postemergence (POST). The adoption of the type of system will depend on the investment capacity of the producer, skilled workforce available, and the modality of application. Although it still has some limitations, VRA has been widespread and has been occupying more and more space in chemical management, the tendency in the medium- and long term is that there is a gradual replacement of the conventional method of application. Given the benefits provided by VRA along with the engagement of companies and researchers, there will be constant evolution and improvement of this technology, cheapening the costs of implementation and providing its adoption by an increasing number of producers. Thus, the objective of this chapter was to address an overview of the use of herbicides in VRA for weed management in PRE and POST.

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Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2017154-10986 ◽

2018 ◽

Vol 15 (4) ◽

pp. e0209 ◽

Cited By ~ 3

Author(s):

Anna Vatsanidou ◽

George D. Nanos ◽

Spyros Fountas ◽

John Baras ◽

Anamaria Castrignano ◽

...

Keyword(s):

Precision Agriculture ◽

Organic Matter Content ◽

Climatic Conditions ◽

Matter Content ◽

Management Approach ◽

Variable Rate ◽

Growing Seasons ◽

Variable Rate Application ◽

Small Hand ◽

Pear Orchard

Precision agriculture is a management approach for sustainable agriculture. It can be applied even in small fields. It aims to optimize inputs, improve profits, and reduce adverse environmental impacts. In this study, a series of measurements were conducted over three growing seasons to assess variability in a 0.55 ha pear orchard located in central Greece. Soil ECa was measured using EM38 sensor, while soil samples were taken from a grid 17 × 8 m and analysed for texture, pH, P, K, Mg, CaCO3, and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. Soil and yield maps showed considerable variability while fruit quality presented small variations across the orchard. Yield fluctuations were observed, possibly due to climatic conditions. Prescription maps were developed for nitrogen variable rate application (VRA) for two years based on the replacement of the nutrients removed by the crop. VRA application resulted in 56% and 50% reduction of N fertiliser compared to uniform application.

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