Chapter 6

Remote Sensing in Precision Agriculture

Remote Sensing Applications in Precision Agriculture

Precision agriculture (PA) entails the application of a suite of such technologies, such as geospatial technologies, Internet of Things (IoT), big data analysis, and artificial intelligence (AI), which can be utilized to make informed management decisions aimed at increasing 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. The 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) images has promoted remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application, disease and pest management, and yield prediction. The application of remote sensing in agriculture, i.e., in crops and soils, is extremely complex because of the highly dynamic and inherent complexity of biological materials and soils. However, remote-sensing technology provides many advantages over traditional methods in agricultural resource surveys. The benefits include (1) the capability of synoptic view, (2) the potential for a fast survey, (3) the capability of repetitive coverage to detect changes, (4) low-cost involvement, (5) higher accuracy, and (6) use of hyperspectral data for increased information.

Irrigation Management

Application time and irrigation rate play an important role in mitigating crop water stress and achieving optimum crop growth and yield. Farmers use a variety of irrigation management practices depending on many factors, including water availability, existing water management infrastructure at the farm (e.g., storage and conveyance system, type of irrigation system), local/regional water laws, economic status, size of the farm, knowledge of farmer, and others. Many farmers apply uniform irrigation at regular intervals based on their prior knowledge or experience of farming, soils, and climate at the location.

Soil Moisture Estimation

Remote sensing products in optical, thermal, or microwave bands have been used to develop and test multiple indices and techniques for precision water management. For example, Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI), developed from optical images, can be used to diagnose water stress and soil moisture conditions for many crops. Thermal remote sensing–based Crop Water Stress Index (CWSI) is a popular indicator used to estimate irrigation water demand and scheduling. Microwave sensors can accurately measure soil moisture levels by analyzing the reflected or emitted microwave radiation from the soil surface.

Plant Water Status

Remote sensing techniques, specifically thermal infrared remote sensing, can be used to assess plant water status. This is done by measuring the temperature of the plant canopy, which is influenced by the plant's transpiration rate and overall water status. When plants are under water stress, their transpiration rate decreases, leading to higher canopy temperatures. Plants lose water through tiny pores on their leaves (stomata) in a process called transpiration.

Monitoring Crop Health

Remote sensing is used to identify stressed areas in fields by first establishing the spectral signatures of healthy plants. The spectral signatures of stressed plants appear altered from those of healthy plants. The pigmentation of the plants controls this reflectivity and absorption, creating incident radiation depending on the plant’s size, orientation, and color. Plant pigment heavily relies on the amount of chlorophyll, which intensely absorbs radiation within the visible spectrum. When a plant is stressed, chlorophyll production declines, increasing the reflectance of wavelengths in the visible spectrum, including those in the red bands.

Insect and Disease Management

Diseases and insects can cause a significant loss of crop production and farm profits. Early detection of plant pests and their spatial extent can help contain the insect and disease spread and reduce production losses. Field scouting, a conventional method of pest and disease detection, is time-consuming, labor-intensive, and prone to human error. In addition, with field scouting, it may be difficult to detect the pest or disease during the early stages when the symptoms are not fully visible.

Sensors

Sensors (RGB, multispectral, hyperspectral, thermal, and fluorescence) have been developed, representing emerging tools for detecting, identifying, and quantifying plant pests and diseases. Optical RGB cameras often capture images within the 400 to 700 nm wavelength range, being able to record changes in the visible light range, with slight variations in spectral sensitivity observed across smartphone cameras.

Mapping Field Topography

Creating topographical maps using airborne sensing platforms significantly advances irrigation and drainage management. Sensors can give an overview of the land’s drainage patterns, highlighting where water might accumulate and where the field may be vulnerable to erosion. Airborne sensing platforms equipped with advanced sensors, such as LiDAR (Light Detection and Ranging), capture precise details about the terrain, including elevation changes and landforms critical for understanding how water flows across a field. High-resolution elevation data collected by airborne sensing platforms allows farmers to see the fine corrugation of their land that traditional methods might overlook.

Crop Yield Monitoring

Remote sensing also allows farmers and experts to predict the expected crop yield from a given farmland by estimating the quality of the crop and the extent of the farmland. This is then used to determine the overall expected yield of the crop. Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the Leaf Area Index (LAI). In plant biology, LAI stands for Leaf Area Index.

Mapping Soil Organic Carbon

Soil organic carbon (SOC) is certainly one of the most crucial indicators of soil fertility. Estimating it can be accomplished through various methods and technologies (e.g., soil sampling and analysis). Remote sensing technology, particularly utilizing satellite imagery, is a valuable tool for mapping soil organic carbon (SOC) levels by analyzing spectral information from different wavelengths, allowing researchers to estimate SOC content across large areas without the need for extensive ground sampling, thereby providing a cost-effective and efficient way to monitor soil health and carbon sequestration potential at regional and global scales.

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