A5035016168- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Sugarcane Cultivation and Processing
- Advanced Chemical Sensor Technologies
- Effects of Environmental Stressors on Livestock
- Coconut Research and Applications
- Peanut Plant Research Studies
- Genetic and phenotypic traits in livestock
- Cocoa and Sweet Potato Agronomy
- Irrigation Practices and Water Management
- Crop Yield and Soil Fertility
- Industrial Vision Systems and Defect Detection
- Biofuel production and bioconversion
- Forest ecology and management
- Species Distribution and Climate Change
- Plant Pathogens and Fungal Diseases
- Plant Surface Properties and Treatments
- Agricultural Engineering and Mechanization
- Insect Utilization and Effects
- Innovations in Aquaponics and Hydroponics Systems
- Agriculture and Biological Studies
Kansas State University
2022-2024
Universidade Estadual Paulista (Unesp)
2020-2023
Abstract Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical effectively monitoring crop growth maximizing yield. Studies have explored the relationship between vegetation indices (VIs) traits using drone imagery. However, there a gap in literature regarding data availability, accessible datasets. Based on this context, we conducted systematic review to retrieve relevant worldwide state of art drone-based trait assessment. The final dataset...
Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing on-field management, particularly sugarcane. We focus search period of 2016–2021 to refer broadest bibliometric collection, emergence term “UAV” in typical sugarcane latest year complete publication. are capable...
Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of is poor or moment flying an aerial platform not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how combination pixel size (3.5, 6.0 and 8.2 cm) height plant (0.5, 0.9, 1.0, 1.2 1.7 m) could impact on unmanned vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger plant, less accurate prediction. Error was more likely occur regions field...
Sugarcane harvester cutting blade wear increases ratoon damages and losses, impairing sugarcane regrowth. This study aimed to evaluate the quality of basal cut using damage loss indexes correlating them with effects on Harvest parameters such as plant height position, indexes, stem length were evaluated every 30 min, following statistical process control (SPC) assumptions. Blade was examined during three harvesting shifts (0-8 h, 8-16 16-24 h). regrowth assessed by counting number tillers...
Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote distinguishing cultivars is still at an early stage of development, driving need further in-depth investigation. The primary objective this study was therefore analyze it could be possible discriminate market-grade upon from unmanned vehicle (UAV). A secondary time day impact...
Assessing planting to ensure well-distributed plants is important achieve high yields. Digital farming has been helpful in these field assessments. However, techniques are at most times not available for smallholder farmers or low-income regions. Thus, contribute such producers, we developed two methods assess intra-row spacing commercial fields using mobile photos and simple image processing. We assessed a maize after mechanized 7 12 days (DAP) systems (conventional no-till) acquire images...
The use of machine learning techniques to predict yield based on remote sensing is a no-return path and studies conducted farm aim help rural producers in decision-making. Thus, commercial fields equipped with technologies Mato Grosso, Brazil, were monitored by satellite images cotton using supervised techniques. objective this research was identify how early the growing season, which vegetation indices algorithms are best at level. For that, we went through following steps: 1) We observed...
The use of machine learning techniques to predict yield based on remote sensing is a no-return path and studies conducted farm aim help rural producers in decision-making. Thus, commercial fields equipped with technologies Mato Grosso, Brazil, were monitored by satellite images cotton using supervised techniques. objective this research was identify how early the growing season, which vegetation indices algorithms are best at level. For that, we went through following steps: 1) We observed...