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

Agriculture ◽  
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.

Variable-rate application technologies in precision agriculture

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

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

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 645
Author(s):  
S. Hamed Javadi ◽  
Angela Guerrero ◽  
Abdul M. Mouazen
Keyword(s):  
Feature Selection ◽  
Precision Agriculture ◽  
Input Data ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Variable Rate ◽  
Yield Data ◽  
Data Format ◽  
Variable Rate Application ◽  
Cross Correlation Analysis

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.

Strategies for variable rate nitrogen fertilization in hummocky terrain

1997 ◽  
Vol 77 (4) ◽  
pp. 589-595 ◽  
Author(s):  
H. J. Beckie ◽  
A. P. Moulin ◽  
D. J. Pennock
Keyword(s):  
Triticum Aestivum ◽  
Precision Agriculture ◽  
Linum Usitatissimum ◽  
Black Soil ◽  
Variable Rate ◽  
Fertilizer N ◽  
Management Zones ◽  
Fertilizer Use ◽  
Net Returns ◽  
Use Efficiency

A study was conducted from 1994 to 1996 in a hummocky landscape near Prince Albert, Saskatchewan in the moist Black soil climatic zone to determine the best criterion for defining fertilizer management zones within a field and how much fertilizer to apply in each zone. A uniform rate fertilization (CF) treatment was compared with three variable rate fertilization (VRF) treatments that used management zones based on soil residual nitrate-N (VRFrn), organic carbon (VRFom) and topography (VRFt). For VRFom and VRFt, fertilizer recommendations were based on soil residual N levels within zones and yield potentials that differed between zones. Flax (Linum usitatissimum) was grown in 1994, spring wheat (Triticum aestivum) in 1995, and canola (Brassica rapa) in 1996. Fertilizer use efficiency (FUE), defined as kilograms seed per kilogram fertilizer N, was markedly higher for VRFom and VRFt than CF or VRFrn. This enhanced FUE resulted in net returns, defined as crop revenue minus fertilizer cost, of about $10 ha−1 more than that of CF. Three successive years of VRF in this study suggests that this practice can enhance the efficient use of fertilizer N and has potential to increase profitability of fertilizer use, by more closely matching fertilizer N inputs with crop nutrient requirements. Key words:Brassica rapa, Linum usitatissimum, Triticum aestivum, nitrogen, variable rate fertilization, precision agriculture

Development of a Variable-Rate Controller for a Low-Cost Precision Planter

2020 ◽  
Vol 36 (2) ◽  
pp. 233-243
Author(s):  
Andre L.de F. Coelho ◽  
Daniel M. de Queiroz ◽  
Domingos S.M. Valente ◽  
Francisco A. C. Pinto
Keyword(s):  
Angular Velocity ◽  
Low Income ◽  
Precision Agriculture ◽  
Smallholder Farmers ◽  
Low Cost ◽  
Field Tests ◽  
Planting Density ◽  
Variable Rate ◽  
Management Zones ◽  
Seed Metering Device

HighlightsA low-cost controller for variable-rate seeding was developed.The controller successfully identified management zones and changed the angular velocity of the seed metering device.The variable-rate controller maintained the actual seeding rate according to the prescribed seeding map.Abstract. The use of machines for variable-rate applications is becoming popular in modern agriculture. Due to the presence of imported and complex components, the acquisition cost of these machines is high for smallholder farmers. Several studies have been carried out using low-cost components in the development of precision agriculture machines to facilitate their adoption in low-income agriculture. Thus, the objective of this work was to develop a variable-rate controller for a low-cost precision planter. The system was developed and installed on a 1-row manual planter with a horizontal perforated disk distributor. A direct-current electric motor was used to drive the seed metering device. The angular velocity of the electric motor was controlled by a BeagleBone Black single-board computer. A program was written in Python 3.6 language, and a graphical user interface was generated by using PyQt5. Field trials were performed with maize seeds using a 28-hole disk and a prescription seeding map with four management zones. The row spacing was 0.75 m, and the planter ground speed was close to 1.0 m s-1. Field tests showed that the controller was effective at identifying the four management zones and controlling the angular velocity of the motor. By counting the number of plants germinated in the field test, it was verified that the variation in the angular velocity of the motor produced a change in the planting density. At each management zone, the planting density corresponded to the prescribed seeding map. The total cost of the parts used to assemble the controller was US$337.97, characterizing it as low cost. Successful field tests showed the potential for using low-cost components to develop variable-rate machines for smallholder farmers. Keywords: Low-income agriculture, Management zones, Precision agriculture, Single-board computer, Smallholder farmers.

scholarly journals Variable Rate Irrigation (VRI)

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.

Remote sensing and implications for variable-rate application using agricultural aircraft

2004 ◽  
Author(s):  
Steven J. Thomson ◽  
Lowrey A. Smith ◽  
Jeffrey D. Ray ◽  
Paul V. Zimba
Keyword(s):  
Remote Sensing ◽  
Variable Rate ◽  
Variable Rate Application

scholarly journals How the Small Object Detection via Machine Learning and UAS-Based Remote-Sensing Imagery Can Support the Achievement of SDG2: A Case Study of Vole Burrows

2021 ◽  
Vol 13 (16) ◽  
pp. 3191
Author(s):  
Haitham Ezzy ◽  
Motti Charter ◽  
Antonello Bonfante ◽  
Anna Brook
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Object Detection ◽  
Precision Agriculture ◽  
Crop Production ◽  
Data Availability ◽  
Small Object ◽  
Multimodal Data ◽  
Robust Detection ◽  
Small Object Detection

Small mammals, and particularly rodents, are common inhabitants of farmlands, where they play key roles in the ecosystem, but when overabundant, they can be major pests, able to reduce crop production and farmers’ incomes, with tangible effects on the achievement of Sustainable Development Goals no 2 (SDG2, Zero Hunger) of the United Nations. Farmers do not currently have a standardized, accurate method of detecting the presence, abundance, and locations of rodents in their fields, and hence do not have environmentally efficient methods of rodent control able to promote sustainable agriculture oriented to reduce the environmental impacts of cultivation. New developments in unmanned aerial system (UAS) platforms and sensor technology facilitate cost-effective data collection through simultaneous multimodal data collection approaches at very high spatial resolutions in environmental and agricultural contexts. Object detection from remote-sensing images has been an active research topic over the last decade. With recent increases in computational resources and data availability, deep learning-based object detection methods are beginning to play an important role in advancing remote-sensing commercial and scientific applications. However, the performance of current detectors on various UAS-based datasets, including multimodal spatial and physical datasets, remains limited in terms of small object detection. In particular, the ability to quickly detect small objects from a large observed scene (at field scale) is still an open question. In this paper, we compare the efficiencies of applying one- and two-stage detector models to a single UAS-based image and a processed (via Pix4D mapper photogrammetric program) UAS-based orthophoto product to detect rodent burrows, for agriculture/environmental applications as to support farmer activities in the achievements of SDG2. Our results indicate that the use of multimodal data from low-cost UASs within a self-training YOLOv3 model can provide relatively accurate and robust detection for small objects (mAP of 0.86 and an F1-score of 93.39%), and can deliver valuable insights for field management with high spatial precision able to reduce the environmental costs of crop production in the direction of precision agriculture management.

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

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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