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

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.

High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images.

Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared. The most sensitive spectral (SVIs) and color vegetation indices (CVIs) for chlorophyll content were extracted and evaluated as reliable estimators of chlorophyll content. Using an ASD FieldSpec 4 Hi-Res spectroradiometer and a portable red, green, and blue (RGB) camera, 3600 hyperspectral reflectance measurements and 800 RGB images were acquired from lettuce grown across a gradient of nutrient levels. Ground measurements of leaf chlorophyll were acquired using an SPAD-502 m calibrated via laboratory chemical analyses. The results revealed a strong relationship between chlorophyll content and SPAD-502 readings, with an R2 of 0.95 and a correlation coefficient (r) of 0.975. The developed AutoML models outperformed all traditional models, yielding the highest values of the coefficient of determination in prediction (Rp2) for all vegetation indices (VIs). The combination of SVIs and CVIs achieved the best prediction accuracy with the highest Rp2 values ranging from 0.89 to 0.98, respectively. This study demonstrated the feasibility of spectral and color vegetation indices as estimators of chlorophyll content. Furthermore, the developed AutoML models can be integrated into embedded devices to control nutrient cycles in aquaponics systems.

Design and simulation for seeding performance of high-speed inclined corn metering device based on discrete element method (DEM).

Mechanical precision corn seed-metering planter has a compact structure, missed and repeated seeding advantages during high-speed operation. In this regard, the current research study focuses on the development of a corn seed planter that features an inclined seed-metering device. The spatial layout of the seed-metering device is optimized to change the seed-filling mode to meet the needs of high-speed operation. Firstly, the mechanical characteristics and seeds in the metering device chamber were analyzed, and then the seed-filling stress model was established. Secondly, a mechanical model for corn seed particles was developed for virtual simulation tests and numerical analysis using the discrete element method (DEM) and EDEM software. Moreover, a quadratic rotating orthogonal center combination test was implemented by setting the inclination angle of seed-metering device θ(A), machine ground speed v(B), and rotation speed of metering disc n(C) as the influence factors, with the missed seeding rate M and the seed-filling stress S as the evaluation indices. The results indicated that the most significant factors affecting the missed seeding rate, seed-filling stress, S, were the rotation speed of the metering disc (n) > machine ground speed (v) > inclination angle of the metering disc (θ) and inclination angle of the metering device (θ) > rotation speed of the metering disc (n) > machine ground speed (v), respectively. However, the field verification test shows that the optimized corn seed-metering planter achieved mean values of M = 4.33, Q(qualified seeding rate) = 92.83%, and R(repeated seeding rate) = 2.84%, with average relative errors of 1.17% compared to the simulation tests and the accuracy and effectiveness of the DEM simulation model was verified. Therefore, the developed corn seed-metering device meets the industry standards and operation requirements for precise corn sowing, and technical support can be given for future studies of similar precision seeding equipment.

Soil carbon mineralization in response to nitrogen enrichment in surface and subsurface layers in two land use types.

Atmospheric nitrogen (N) deposition increases N availability in soils, with consequences affecting the decomposition of soil carbon (C). The impacts of increasing N availability on surface soil C dynamics are well studied. However, subsurface soils have been paid less attention although more than 50% soil C stock is present below this depth (below 20 cm). This study was designed to investigate the response of surface (0-20 cm) and subsurface (20-40 cm and 40-60 cm) C dynamics to 0 (0 kg N ha-1), low (70 kg N ha-1) and high (120 kg N ha-1) levels of N enrichment. The soils were sampled from a cropland and a grass lawn and incubated at 25 °C and 60% water holding capacity for 45 days. Results showed that N enrichment significantly decreased soil C mineralization (Rs) in all the three soil layers in the two studied sites (p < 0.05). The mineralization per unit soil organic carbon (SOC) increased with profile depth in both soils, indicating the higher decomposability of soil C down the soil profile. Moreover, high N level exhibited stronger suppression effect on Rs than low N level. Rs was significantly and positively correlated with microbial biomass carbon explaining 80% of variation in Rs. Overall; these results suggest that N enrichment may increase C sequestration both in surface and subsurface layers, by reducing C loss through mineralization.