A review the role of remote sensing in precision agriculture


Remote sensing provides input data for many precision agriculture applications including pre-growth soil fertility and moisture analyses, crop growth and growth detractant monitoring (crop scouting), and yield forecasting. This information in turn helps the farm producer in his decision-making.


What are remote sensing systems used in precision agriculture?

Remote Sensing Systems Used in Precision Agriculture Remote sensing systems used for PA, and agriculture in general, can be classified based on (i) sensor platform and (ii) type of sensor. Sensors are typically mounted on satellites, aerial, and ground-based platforms (Figure 1).

When did satellite remote sensing start in agriculture?

However, the era of satellite remote sensing for agriculture started with the launch of Landsat 1 (formerly known as the Earth Resources Technology Satellite—ERTS) on 23 July 1972 by the National Aeronautics and Space Administration (NASA). Remote Sens.2020,12, 3136 6 of 32

How can remote sensing help with variable rate irrigation?

Remote sensing data can help discern the variability within the field and apply variable rate irrigation with commonly used irrigation systems such as a center pivot.

How many remote sensing data are used to estimate soil moisture?

Remote Sens.2020,12, 3136 14 of 32 5.1.3. Soil Moisture Remote sensing data acquired in multiple bands, including optical, thermal, and microwave, have been used to estimate soil moisture globally [28,193,213].


What is the role of remote sensing products for precision farming?

Remote sensing technologies and tools enable farmers to characterize spatial variability in fertilizers among farms and crop fields. They also estimate the number of fertilizers, herbicides, and insecticides to be used on a farm.

What is remote sensing role in agriculture?

Remote sensing gives the soil moisture data and helps in determining the quantity of moisture in the soil and hence the type of crop that can be grown in the soil. 11. Irrigation monitoring and management: Remote sensing gives information on the moisture quantity of soils.

What is the role of remote sensing and GIS in agriculture?

Crop inventory Remote sensing (RS) and Geographical Information System (GIS) play a crucial role for identification of crops and areas where changes in cropping patterns and useful tool to carry out crop surveys and mapping [4].

What is the main purpose of precision agriculture?

Precision agriculture (PA) is an approach to farm management that uses information technology (IT) to ensure that crops and soil receive exactly what they need for optimum health and productivity. The goal of PA is to ensure profitability, sustainability and protection of the environment.

How can remote sensing improve agricultural outcomes?

With remote sensing method, the form of crops developed in an area, crop state, and yield can be considered. Recording crop state by remote sensing can get the crop status in addition to the condition and progress of their development.

What are the benefits of agriculture sensors?

Sigfox-enabled sensors offer more precise monitoring of weather conditions for better predictions of crop needs. Weather monitoring can help cut costs, product higher crop yields, and prevent over or underwatering. Sensors allow farmers to make better decisions about pesticides, watering, and preventing disease.

What is the application of GIS in agriculture?

GIS has the capability to analyze soil data and determine which crops should be planted where and how to maintain soil nutrition so that the plants are best benefitted. GIS in agriculture helps farmers to achieve increased production and reduced costs by enabling better management of land resources.

What is precision agriculture explain in detail?

Precision agriculture can be defined as “the application of modern information technologies to provide, process and analyze multisource data of high spatial and temporal resolution for decision making and operations in the management of crop production” (National Research Council, 1997).

What is precision agriculture discuss in detail?

Precision agriculture (PA), as the name implies, refers to the application of precise and correct amounts of inputs like water, fertilizers, pesticides etc. at the correct time to the crop for increasing its productivity and maximizing its yields.

What is precision agriculture and why is it important PDF?

Precision agriculture is a management concept, which relies on intensive data collection and data processing for guiding targeted actions that improve the efficiency, productivity, and sustainability of agricultural operations.

What are the applications of remote sensing in agriculture?

Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

How does remote sensing work?

Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality.

What are the lessons learned from the ongoing investigation at Cal Poly Pomona?

This paper presents the lessons learned from the ongoing investigation at Cal Poly Pomona on the effectiveness of UAV-based remote sensing technology in detecting plant stresses due to water and nutrients. UAVs equipped with multispectral/hyperspectral sensors and RGB cameras were flown over lettuce and citrus plants at Cal Poly Pomona’s Spadra farm. The spectral sensor data were used in the determination of various vegetation indices that provide information on the water and nitrogen stresses of the plants. Proximal sensors that were used for the verification of remote sensing data included water potential meter, chlorophyll meter, and handheld spectroradiometer. The paper shows the relationship between the remote sensing and proximal sensor data. The paper also discusses the flight test procedures, data collection methods, and lessons learned so far.

What is Sentinel 1 used for?

This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency’s Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies.

How can precision farming be used to improve sustainability?

The right time application of the right amount of input is a prerequisite to optimizing profitability and sustainability with a lesser impact on environmental degradation. Such can be achieved through precision farming (PF). It can offer a great potential to minimize the yield gap by optimizing food production using best management practices. It can also help to maintain the consumption of natural resources at an ecologically benign and environmentally sustainable level. PF is a holistic approach to enhance crop productivity with the aid of satellite-based technology and information technology (IT) to assess and manage the spatial and temporal variability of resources and inputs such as seeds, fertilizers, chemicals, etc. within the field. Application of remote sensing (RS) and geographic information system (GIS) shows a great promise to precision agriculture (PA) because of its role in monitoring spatial variability overtime at high resolution. This chapter highlights various applications of RS and GIS techniques in PA or smart agriculture.

What are the biophysical parameters of rice?

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

What is precision agriculture?

Precision agriculture involves the integration of new technologies including Geographic Information Systems (GIS), Global Positioning Systems (GPS) and Remote Sensing (RS) technologies to allow farm producers to manage within field variability to maximize the cost-benefit ratio, rather than using the traditional whole-field approach. Variable Rate Technology (VRT) available with farm implements, such as fertilizer or pesticide applicators and yield monitors, have evolved rapidly and have fostered the growth of precision agriculture. Site specific management allows inputs to be reduced, while optimizing outputs, both of which are attractive to the farm producer. At the same time, by reducing inputs, the run-off of fertilizers and pesticides is reduced, thus improving the environmental condition of the agro-ecosystem. Remote sensing provides input data for many precision agriculture applications including pre-growth soil fertility and moisture analyses, crop growth and growth detractant monitoring (crop scouting), and yield forecasting. This information in turn helps the farm producer in his decision making. Although the acceptance and growth of precision agriculture has been rapid some fundamental requirements are needed to help fully develop and implement this technology. Among these requirements are continued research and development of algorithms for the radiometric and geometric correction of remote sensing data and for information extraction. Also, access to timely, cost-effective remote sensing data, or derived value-added products and the development of decision support making systems or other expert systems integrating GIS, GPS, and RS technologies in a user-friendly fashion are needed. A subsequent training and technology transfer program to accelerate the acceptance and implementation of this technology for the agri-business sector is also a necessity.

How does precision agriculture help the environment?

Precision Agriculture (PA) is changing the way people farm as it offers a myriad of potential benefits in profitability, productivity, sustainability, crop quality, environmental protection, on-farm quality of life, food safety and rural economic development. PA is an innovative, integrated and internationally standardized approach aiming to increase the efficiency of resource use and to reduce the uncertainty of decisions required to manage variability on farms. PA has been hailed as one of the most scientific and modern approaches to production agriculture in the 21st century, as it epitomizes a better balance between reliance on traditional knowledge and information and management-intensive technologies. At present, there is an increasing commitment to reduce reliance on excessive chemical inputs in agriculture. Numerous technologies have been applied to make agricultural products safer and to lower their adverse impacts on the environment, a goal that is consistent with sustainable agriculture.

What is precision agriculture?

Precision Agriculture or Precision Farming is a concept of using the new technologies and collected field information doing the right thing, in the right place, at right time. It is a production system that promotes variable management practices within a field, according to site conditions. This system is based on new tools and sources of information provided by modern technologies. These include the global positioning system (GPS), geographic information systems (GIS), yield monitoring devices, soil, plant and pest sensors, remote sensing (RS), and variable rate technologies for applicators of inputs (Santosh et al. 2003). RS techniques play an important role in assessing crop condition and yield forecasting, acreage estimates of specific crops, detection of crop pests and diseases, disaster location and mapping, wild life management, water supply information and management, weather forecasting, range land management, and livestock surveys (Patil and Chetan 2017). Remote sensing technologies can provide an automatic and objective alternative to visual disease assessment of plant diseases (Mahlein et al. 2012). Production losses have been recorded in agricultural industries worldwide due to various diseases in plants, According to a study by (Huang et al. 2012). The Normalized Difference Vegetation Index (NDVI) is the most popular vegetation index that is extensively used to find the content of green in PA applications. It uses Red (R) and Near Infrared (NIR) channels to compute the NDVI index. More NIR light is absorbed by healthy vegetation; however, absorption ratio is very small for red light (Shafi et al. 2019). It is concluded that remote sensing can provide accurate information to guide agronomic and economic decision making. Precision agriculture is mainly drawn from developed countries, but in India there is still lack of adoption of the technology because of lack of knowledge and awareness among the farmers.


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