Application of Sensors to Assess Soil Conditions in a Korean Paddy Field

2006 ◽  
Vol 321-323 ◽  
pp. 1213-1216
Author(s):  
Sun Ok Chung ◽  
Byong Hak Chong ◽  
Suk Won Kang ◽  
Gi Young Kim
Keyword(s):  
Data Collection ◽  
Precision Agriculture ◽  
Crop Production ◽  
Soil Property ◽  
Recent Trend ◽  
Soil Conditions ◽  
Site Specific ◽  
Optimum Sampling ◽  
Field Information ◽  
Korean Rice

Precision agriculture, also called as site-specific crop (or field) management, is a recent trend in crop production that uses field information collected at different within-field locations to optimize amount, timing, and location of agricultural inputs according to the site-specific requirements. Recent development of soil property sensors has facilitated sensor-based data collection for SSCM in many countries around the world. In this study, commercial soil strength, electrical conductivity, and water content and temperature sensors were applied to a Korean rice (Oriza Sativa L) field and spatial and non-spatial statistical techniques were used to assess soil conditions and the variability, and investigate optimum sampling intensity. Results of the study would be useful for establishment of data collection schemes and better application of soil property sensors to Korean paddy fields for successful precision agriculture.

Effect of Heterogeneous Field Conditions on Corn Seeding Depth Accuracy and Uniformity

2018 ◽  
Vol 34 (5) ◽  
pp. 819-830 ◽  
Author(s):  
Aurelie M. Poncet ◽  
John P. Fulton ◽  
Timothy P. McDonald ◽  
Thorsten Knappenberger ◽  
Joey N. Shaw ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Yield Potential ◽  
Unit Weight ◽  
Soil Conditions ◽  
Volumetric Soil Water Content ◽  
Site Specific ◽  
Growing Seasons ◽  
Unit Depth ◽  
Planter Performance ◽  
Volumetric Soil Water

Abstract. Optimization of planter performance such as uniform seeding depth is required to maximize crop yield potential. Typically, seeding depth is manually adjusted prior to planting by selecting a row-unit depth and a row-unit downforce to ensure proper seed-soil contact. Once set, row-unit depth and downforce are usually not adjusted again for a field although soil conditions may vary. Optimization of planter performance requires automated adjustments of planter settings to varying soil conditions, but development of precision technologies with such capabilities requires a better understanding of soil-planter interactions. The objective of this study was to evaluate seeding depth response to varying soil conditions between and within fields and to discuss implications for development and implementation of active planting technologies. A 6-row John Deere MaxEmerge Plus planter equipped with heavy-duty downforce springs was used to plant corn ( L.) in central Alabama during the 2014 and 2015 growing seasons. Three depths (4.4, 7.0, and 9.5 cm) and three downforces (corresponding to an additional row-unit weight of 0.0, 1.1, and 1.8 kN) were selected to represent common practices. Depth and downforce were not readjusted between fields and growing seasons. Seeding depth was measured after emergence. Corn seeding depth significantly varied with heterogeneous soil conditions between and within fields and the planter failed to achieve uniform seeding depth across a field. Differences in corn seeding depth between fields and growing seasons were as high as 2.1 cm for a given depth and downforce combination. Corn seeding depth significantly co-varied with field elevation but not with volumetric soil water content. Seeding depth varied with elevation at a rate ranging from -0.1 cm/m to -0.6 cm/m. Seeding depth co-variation to field elevation account for some but not all site-specific seeding depth variability identified within each field trial. These findings provide a better understanding of site-specific seeding depth variability and issues to address for the development of site-specific planting technologies to control seeding depth accuracy and improve uniformity. Keywords: Depth control, Downforce, Planter, Precision agriculture, Seeding depth, Uniformity.

scholarly journals Challenges and limtations of site specific crop production applications of wheat and maize

2012 ◽  
pp. 101-104
Author(s):  
István Balla ◽  
Ákos Tarnawa ◽  
Csaba Horváth ◽  
Judit Kis ◽  
Márton Jolánkai
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Production Management ◽  
Plant Density ◽  
Triticum Aestivum L ◽  
Site Specific ◽  
Small Plot ◽  
Management Techniques ◽  
The Impact

The development and implementation of precision agriculture or site-specific farming has been made possible by combining the Global Positioning System (GPS) and the Geographic Information Systems (GIS). Site specific agronomic applications are of high importance concerning the efficiency of management in crop production as well as the protection and maintenance of environment and nature. Precision crop production management techniques were applied at four locations to evaluate their impact on small plot units sown by wheat (Triticum aestivum L.) and maize (Zea mays L.) in a Hungarian national case study. The results obtained suggest the applicability of the site specific management techniques, however the crops studied responded in a different way concerning the impact of applications. Maize had a stronger response regarding grain yield and weed canopy. Wheat was responding better than maize concerning plant density and protein content performance.

scholarly journals Agriculture historical steps towards Sustainable Precision Agriculture

2020 ◽  
pp. 1
Author(s):  
Marco Vieri ◽  
Daniele Sarri ◽  
Stefania Lombardo ◽  
Marco Rimediotti ◽  
Riccardo Lisci ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Food Safety ◽  
Precision Agriculture ◽  
Crop Production ◽  
Site Specific ◽  
Software Technology ◽  
Action Programme ◽  
American Society ◽  
Agenda 2000 ◽  
Food Safety And Quality

The term precision agriculture were introduced into scientific literature by Jhon Schueller in the 1991 Meeting of the American Society of Agricultural Engineers (ASAE) in Chicago: “the continuous advantages in automation hardware and software technology have made possible what is variously knows as spatially-variable, or site specific crop production”. The concept of sustainable development was introduced in 1987 in the Bruntland Report and the term “sustainable agriculture” was defined in the 5th European Environmental action programme: Towards sustainability. In Agenda 2000, 5 main objectives founded Common Agricultura Policies toward 2020: competitiveness; food safety and quality; farmers’ wellness and proper income; environmental respect; new jobs opportunities for farmers’ communities

Implementing precision agriculture concepts and technologies into crop production and site-specific management of nematodes.

2021 ◽  
pp. 421-427
Author(s):  
Michael T. Plumblee ◽  
John D. Mueller
Keyword(s):  
Precision Agriculture ◽  
Crop Production ◽  
Spatial Location ◽  
Future Research ◽  
Economic Returns ◽  
Site Specific ◽  
Management Zones ◽  
Application Rates ◽  
Other Information ◽  
Specific Management

Abstract Precision agriculture is defined as a management strategy that gathers, processes and analyses temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production. This includes a wide range of technologies, many of which are linked to geographic information system technologies used to analyse spatial location and organize layers of on-farm data. Southern root-knot (Meloidogyne incognita), reniform (Rotylenchulus reniformis), Columbia lance (Hoplolaimus columbus) and sting (Belonolaimus longicaudatus) nematodes are significant problems on cotton in the US. Granular and fumigant nematicides have provided control when applied at uniform rates across fields pre-plant in-furrow or at-plant in-furrow at costs of US$148 and US$74 per hectare, respectively. Site-specific variable-rate (SSVR) technologies offer producers the potential to move away from uniform application rates and apply nematicides only to specific management zones in a field. The goal is to sustain yield levels while minimizing nematicide applications and thus increasing economic returns. This chapter discusses strategies for the development of management zones, evolution of application technologies needed for SSVR applications, assessment of nematode damage from multispectral images, and field experiences with site-specific nematode management. The economic importance of precision agriculture technology and future research requirements are also mentioned.

scholarly journals 643 Site-specific Management for the 21st Century

HortScience ◽  
1999 ◽  
Vol 34 (3) ◽  
pp. 558D-558
Author(s):  
Pierre C. Robert
Keyword(s):  
Precision Agriculture ◽  
New Technologies ◽  
The United States ◽  
Soil Survey ◽  
Soil Conditions ◽  
Agricultural System ◽  
Tree Plantation ◽  
Site Specific ◽  
Record Keeping ◽  
Soil Site

The new agricultural system called soil/site specific crop management (SSCM), now more generally named precision agriculture (precision farming) is the start of a revolution in natural resource management based on INFORMATION TECHNOLOGY AND CONTROL: it is bringing agriculture in the digital and information age. New technologies in the early 80s, particularly the microprocessor, made possible the development in the United States of farm machinery computers and controllers, the electronic acquisition and process of spatial field data to build farm geographic record keeping systems, the production of soil/site specific condition and management maps using GIS, the positioning of machines using GPS, and the development of real-time soil and crop sensors, particularly yield sensors. The concept of precision agriculture originated from a better awareness of soil and crop conditions variability within fields. The variability of soil conditions within parcels in the U.S. has been demonstrated in many ways (soil survey, soil sampling, and remote sensing) for both soil nutrients and soil physical properties (e.g., available water and compaction). It is progressively found that the concept of precision agriculture can be applied to a variety of crops and practices; management technological levels; and farm types and sizes. For example, in addition to grain crops (corn, soybeans, and wheat), applications are now developed for sugar beet and sugar cane, potato, cotton, peanut, vegetables, turf, or- chard, livestock, tree plantation, etc. Precision agriculture is still in infancy but it is the agricultural system of the future because it offers a unique variety of potential benefits in profitability, productivity, sustainability, crop quality, food safety, environmental protection, on-farm quality of life, and rural economic development.

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.

Synergistic Technologies for Precision Agriculture

2021 ◽  
pp. 123-139
Author(s):  
Moses Oluwafemi Onibonoje ◽  
Nnamdi Nwulu
Keyword(s):  
Developing Countries ◽  
Data Collection ◽  
Soil Quality ◽  
Precision Agriculture ◽  
Crop Production ◽  
Developed Countries ◽  
Quality Parameters ◽  
Financial Capital ◽  
Technology Access ◽  
Water Nutrients

Precision agriculture (PA) as a concept allows input optimization by farmers and food producers in order to improve productivity and enhance quality yields while minimizing costs and environmental impacts. Developed countries typically identify with precision agriculture due to very large sizes of farms and the possibility of mechanized systems of crop production. The method involves the data collection, analysis, and plotting on productivity, soil quality parameters, and environmental levels at different locations within the field to decide on the amounts of the applicable inputs (such as water, nutrients, and fertilizers) to the field. In most developing countries, precision agriculture technology is still largely missing. The field sizes are smaller, and technology access, training, and financial capital are still grossly limited. Nonetheless, the farmers in the developing countries still explore the available resources and means at their disposal to increase their agricultural production and productivity.

Determination of Tillage Depth Based on Physical Properties of Soil for Rice Production in Korea

2006 ◽  
Vol 321-323 ◽  
pp. 1229-1232 ◽  
Author(s):  
Lee Yul Kim ◽  
Hyun Jun Cho ◽  
Sun Ok Chung ◽  
Won Yeop Park ◽  
Kyou Seung Lee
Keyword(s):  
Physical Properties ◽  
Crop Production ◽  
Soil Management ◽  
Rice Paddy ◽  
Soil Conditions ◽  
Environmental Damage ◽  
Site Specific ◽  
Rice Growth ◽  
Tillage Depth ◽  
Cone Index

Compaction is becoming a great concern in crop production and the environment. Recently, three has been a need of field management based on site-specific conditions to improve sustainability of agriculture and reduce environmental damage. In the study, soil management or tillage depth was recommended nondestructively based on cone index profiles for typical Korean rice paddy fields. Field variables related to tillage, soil strength, rice growth, and other soil physical properties showed considerable spatial and vertical variations as well as significant (α<0.1) correlations among them. Cone index profiles observed also varied by field sites, and maximum cone index and depth to the maximum cone index showed significant (α<0.1) correlation with tilled depth as well as rice growth and other field variables. When soil management was recommended based on CI measurements, 13.4, 16.8, and 95.3% of the total surveyed areas, and 10.6, 18.9, and 51.6% of the total soil volume were chosen for management depth of 10, 20, 40 cm, respectively, indicating that soils of many field sites would not restrict rice growth. It was concluded that the concept of site-specific soil management based on soil conditions could save labor, time, machine use, and energy.

scholarly journals 648 Practical Applications of Site-specific Management—An Industry Perspective

HortScience ◽  
1999 ◽  
Vol 34 (3) ◽  
pp. 559D-559
Author(s):  
John LeBoeuf
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Management Practices ◽  
Learning By Doing ◽  
Fresh Market ◽  
Site Specific ◽  
Remote Sensing Imagery ◽  
Practical Applications ◽  
Specific Management

The initial surge of interest in precision agriculture technologies exhibited by innovators and early adopters involved in crop production appears to have crossed over an important threshold and made a significant development. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified. Access to accurate information pertaining to practical applications of site-specific management would be expected to motivate more producers to incorporate technology uses with crop production. This next group of producers has been watching technology developments as they preferred to avoid risk and wait for identifiable benefits. Waiting for detailed case studies involving high value fruit and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected, especially in the fresh-market industry that places quality attributes high on the list of desired features. Practical applications of technology that pertain to manageable factors will be the impetus to implementation of site-specific management. High resolution remote sensing imagery from digital aerial and satellite sensors has been used to identify plant stress, direct plant tissue and soil sampling efforts to identifiable soil variability, and provide valuable information for analysis and interpretation of crop growth. Examples of remote sensing imagery that has provided valuable in season progress reports will be identified. Imagery can then be used in a geographic information system along with field maps based on soil properties and physical characteristics determined by on-the-go tractors using various sensors. The focus will be on practice, not theory, as seen from an industry perspective.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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