Precision Agriculture Using Remote Sensing and GIS for Peanut Crop Production in Arid Land

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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Precision agriculture using hyperspectral remote sensing and GIS

Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005. ◽

10.1109/rast.2005.1512537 ◽

2006 ◽

Cited By ~ 4

Author(s):

H. Cetin ◽

J.T. Pafford ◽

T.G. Mueller

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Remote Sensing And Gis ◽

Hyperspectral Remote Sensing

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Practical Applications of Remote Sensing Technology—An Industry Perspective

HortTechnology ◽

10.21273/horttech.10.3.475 ◽

2000 ◽

Vol 10 (3) ◽

pp. 475-480 ◽

Cited By ~ 3

Author(s):

John LeBoeuf

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Crop Production ◽

Management Practices ◽

Plant Stress ◽

Economic Benefits ◽

Learning By Doing ◽

Accurate Information ◽

Fresh Market ◽

Practical Applications

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. As valuable field experience increases and learning by doing advances, successful applications of management practices are being identified even though few are adequately documented with economic benefits. Access to accurate information pertaining to 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 fruits and vegetables may be the wrong approach to take. Fierce competition and strict confidentiality are expected in the fresh market industry. Thus, personal experience with technology becomes more relevant to innovative producers than published literature. This is especially true in California where 350 different crops are produced. High resolution imagery from digital aerial and satellite sensors has been used in crop production in California to identify plant stress, direct plant tissue and soil sampling efforts, and provide information for analysis and interpretation of crop growth. Examples of remote sensing imagery that have provided valuable in-season progress reports will be identified. The focus will be on practice, not theory, as seen from an industry perspective.

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Applications of Remote Sensing in Precision Agriculture: A Review

Remote Sensing ◽

10.3390/rs12193136 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3136

Author(s):

Rajendra P. Sishodia ◽

Ram L. Ray ◽

Sudhir K. Singh

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Data Storage ◽

Precision Agriculture ◽

Crop Production ◽

Irrigation Management ◽

Green Revolution ◽

Vegetation Indices ◽

Past Century ◽

The Past

Agriculture provides for the most basic needs of humankind: food and fiber. The introduction of new farming techniques in the past century (e.g., during the Green Revolution) has helped agriculture keep pace with growing demands for food and other agricultural products. However, further increases in food demand, a growing population, and rising income levels are likely to put additional strain on natural resources. With growing recognition of the negative impacts of agriculture on the environment, new techniques and approaches should be able to meet future food demands while maintaining or reducing the environmental footprint of agriculture. Emerging technologies, such as geospatial technologies, Internet of Things (IoT), Big Data analysis, and artificial intelligence (AI), could be utilized to make informed management decisions aimed to increase crop production. Precision agriculture (PA) entails the application of a suite of such technologies to optimize agricultural inputs to increase agricultural production and reduce input losses. Use of remote sensing technologies for PA has increased rapidly during the past few decades. The unprecedented availability of high resolution (spatial, spectral and temporal) satellite images has promoted the use of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. In this paper, we provide an overview of remote sensing systems, techniques, and vegetation indices along with their recent (2015–2020) applications in PA. Remote-sensing-based PA technologies such as variable fertilizer rate application technology in Green Seeker and Crop Circle have already been incorporated in commercial agriculture. Use of unmanned aerial vehicles (UAVs) has increased tremendously during the last decade due to their cost-effectiveness and flexibility in obtaining the high-resolution (cm-scale) images needed for PA applications. At the same time, the availability of a large amount of satellite data has prompted researchers to explore advanced data storage and processing techniques such as cloud computing and machine learning. Given the complexity of image processing and the amount of technical knowledge and expertise needed, it is critical to explore and develop a simple yet reliable workflow for the real-time application of remote sensing in PA. Development of accurate yet easy to use, user-friendly systems is likely to result in broader adoption of remote sensing technologies in commercial and non-commercial PA applications.

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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

Remote Sensing ◽

10.3390/rs13163191 ◽

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.

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

HortScience ◽

10.21273/hortsci.34.3.559d ◽

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.

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