Advantages of LiDAR sensors in agriculture


Using new sensor technology in agriculture can significantly increase yields and assist farmers in making better use of land. It represents an important step in preparing the industry for the future.

Notably, LiDAR, which stands for Light Detection and Ranging, is one of the most advanced and most accurate technologies in a Geographic Information System (GIS) used in agriculture.

It is a remote sensing method that measures variable distances of the Earth by using light in the form of a pulsed laser. LiDAR can smartly improve the agriculture system with 3D modeling, yield forecasting, soil type determination, and phenomics study. Even in difficult weather and lighting conditions, it provides a precise 3D measurement of a water flow, water catchment area, location of tree and crop, and their accurate plant population, water flow direction at the base of each tree.

Below are some of the key advantages of using LiDAR in agriculture.

1. 3D modelling of crop field

LiDAR technology can create 3D models of agricultural land and precise maps of the local natural resources. Electronic measurements of canopy traits, greenness, chlorophyll content, soil map, and water 3D map appear to be the most accurate way to provide trustworthy and objective information about the management of crops. The operating parameters of agronomic applications will be immediately modified using this information in real-time. In every instance, LiDAR seems to be the method for measuring canopies that is the most precise. The application of pesticides and irrigation systems are just two examples where the 3D modeling of the crops is crucial. To improve pesticide application, canopy characterization is also crucial in horticulture.

GLS was used to separate maize plants from weeds and soil in field crops so that herbicides could be applied selectively. GLS was used to better apply nitrogen fertilizers by sensing the wheat plants’ nitrogen status. Another method uses GLS to estimate wheat crop density, which could be used to automatically adjust a combined harvester’s speed for a constant intake of biomass.

2. Phenomics study

Plant phenomics is a new way to connect environmental research with plant genomics, which will help with plant management and breeding. “High-throughput plant phenotyping” has improved thanks to remote sensing techniques. Three-dimensional (3D) phenotyping’s accuracy, effectiveness, and applicability remain problematic, particularly in field settings. With the quick development of facilities and algorithms, light detection and ranging (LiDAR) offers a potent new tool for 3D phenotyping.

Numerous initiatives have been made to use LiDAR in agriculture to study static and dynamic changes in structural and functional phenotypes. With easier and less expensive gene association, analysis of environmental practices, and new insights into breeding and management, this advancement also enhances 3D plant modeling across various spatial-temporal scales and disciplines.

3. Determination of soil type and soil analysis

Data can be gathered using LiDAR technology to pinpoint the precise type of soil present on a given farmland. The farmer needs to know this information because it tells them what kinds of crops can be grown on that farm and how much fertilizer is needed. It may benefit 5R stewardship (right time, dose, amount, place, and method). LiDAR sensors can assist experts and farmers in analyzing the soil type and content to determine whether it is suitable for growing crops.

LiDAR data can be used to precisely design and map farmland, as well as to map other types of land. The layout and topography of the farmland will also be included in this data. As per the study, a 2D mean profile view of the soil is created to compare the digital LiDAR. The experiments led to the following conclusion: By increasing the resolution from 2D measurements to 3D scans, the geometric variations of soil texture, water content, flow, slope, and fertility in the direction of the soil are observed more clearly. The findings thus demonstrated the significance of using a non-contact method for precise measurements of soil surface.

4. Smart crop management

Maps of cultivated fields are typically created by manually digitizing the fields using satellite or aerial imagery. Manual ground surveys are then used to assign these images to various crop types. This approach, however, is time-consuming, costly, and prone to human error. Automated remote sensing techniques are more affordable options, like those used by airborne LiDARs. The data gathered with LiDAR can be combined with machine learning algorithms to automate crop classification.

LiDAR can analyze crops, estimate crop quality, compare results to standards, and determine whether a crop is suitable for a given location. By utilizing LiDAR in agriculture, farmers will better understand the current choice of agricultural soil, which crops are suitable for farming at the current stage, and other environmental information about farmland through intelligent analysis and better management. LiDAR also aids in determining the extent to which crops have been damaged and aids farmers in developing a recovery strategy.

5. Yield forecasting

It has been possible to predict expected farm yields using LiDAR data. Farmers can make quicker and more accurate harvesting decisions thanks to information about field yield variability provided by yield monitoring and crop geometric characterization. Fruit maturation can be identified by farmers using LiDAR technology. LiDAR sensors can help farmers increase yield by estimating the yield through field scanning.

Researchers discovered that using LiDAR sensors mounted on drones; they could predict rice yield. Several empirical yield prediction models for rice production were created using five pertinent vegetation indices, including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), rice growth vegetation index (RGVI), moisture stress index (MSI), and leaf area index (LAI).

6. Prevention of soil erosion

The GLS point cloud is used to measure surface properties (roughness) in the runoff zone, which are impossible to measure in the field through 2D mapping and modeling of specific farmland. LiDAR data can be used to quantify soil loss. Farmers can devise preventive measures to lessen or stop soil erosion by learning the exact terrain of the farm and its contours.

7. LiDAR in forestry

Forests can be mapped using LiDAR by measuring the canopy’s vertical organization and density. These models enable us to produce precise forest inventories and comprehend complex structures. LiDAR can track the patterns of forest fires and alert the fire department to the potential occurrence of the next forest fire.

We might be able to boost the site’s productivity regarding the quality of the trees and the overall yield using precision forestry, which is focused on particular forest areas. A ground-based LiDAR system has great potential for determining structural characteristics like volume and forest inventory variables like DBH and tree height. The outcomes demonstrate that GLS can determine forest inventory parameters in a precise, high-resolution, non-destructive manner.