A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture

Abstract

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Keywords: LiDAR technology; autonomous harvesting systems; crop management; disease detection; precision agriculture; yield estimation.

Figure 4

UAV-mounted ALS for canopy height estimation [69].

Figure 1

Use of LiDAR in precision agriculture [51].

Figure 2

Different types of LiDAR systems are used in precision agriculture [57].

Figure 3

Airborne LiDAR system [58].

Figure 5

TLS for crop height estimation [81].

Figure 6

View of apple trees rows as an RGB, point clouds from drones (orange) and LiDAR (blue) superimposed with tree wall height curves [85].

Figure 7

(a) Maize plants (arranged in 8 rows) with additional vegetation, and (b) a 3D model of the segmented point cloud [86].

Figure 8

Mapping an almond orchard using an MLS [103].

Figure 9

Data acquisition using backpack LiDAR system (a) Aerial view of the experimental site (b) Backpack LiDAR system (c) Function and data acquisition (d) 3D reconstruction of the experimental field (e) AGB estimation through point clouds data [107].

Figure 10

An overview of applications of LiDAR in agriculture (A) canopy and individual height measurement, (B) growth prediction and measurement, (C) field management practices, (D) breeding, (E) crop management practices) [57].

Figure 11

Phenotyping system and scanning areas of LiDAR and ultrasonic sensors [145].

Figure 12

(A) LiDAR and (B) photogrammetry time-series point cloud data colored by survey (dark blue to red) [149].

Figure 13

Conceptual identification and removal of wild plants using an ALS and a TLS (a) Weed detection via ALS (b) Signals transfer to TLS (c) Navigation to exact location (d) Removal of weeds via TLS [152].

Figure 14

Measurement of LAI using UAV–LiDAR systems [160].

Figure 15

Comparing four soil moisture modeling methods [193].

Figure 16

Different crop densities and combine harvester with LiDAR system (a) 100 ears per m²; (b) 200 ears per m²; (c) 300 ears per m²; (d) 400 ears per m² [194].

Figure 17

Multi-beam LiDAR system for the detection of fruits [200].

Figure 18

Conceptual architecture system of the integration of three parts [207].

Figure 19

Multi-sensor system for post-harvest assessment of environmental stress in wheat [208].