Development of a PWM Precision Spraying Controller for Unmanned Aerial Vehicles

This paper presents a new Pulse Width Modulation (PWM) controller for Unmanned Aerial Vehicle (UAV) precisionsprayer for agriculture using a TL494 fixed-frequency pulse width modulator together with a data acquisition board and developedsoftware. An UAV can be remotely controlled or flown autonomously by pre-programmed flight plans. The PWMcontroller was implemented through the guidance system on the UAV with control commands sent between the UAV helicopterand the ground control station via a wireless telemetry system. The PWM controller was tested and validated using LabVIEW8.2. Several analyses were performed in a laboratory to test different control signals. The results show that the PWM controllerhas promise as a higher precision technique for spray applications, which will improve efficiency of pesticide application,especially in crop production areas.

Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management.Compared with piloted aircraft,an Unmanned Aerial Vehicle(UAV)can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision.They can also“fill in the gap”in locations where fixed winged or rotary winged aircraft are not readily available.In agriculture,UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials.Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management.Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well.This paper presents an overview of research involving the development of UAV technology for agricultural production management.Technologies,systems and methods are examined and studied.The limitations of current UAVs for agricultural production management are discussed,as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.

Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle(UAV)

With changing climate and farmland ecological conditions,pest outbreaks in agricultural landscapes are becoming more frequent,increasing the need for improved crop production tools and methods.UAV-based agricultural spraying is anticipated to be an important new technology for providing efficient and effective applications of crop protection products.This paper reviews and summarizes the status of the current research and progress on UAV application technologies for plant protection,and it discusses the characteristics of atomization by unmanned aircraft application systems with a focus on spray applications of agrichemicals.Additionally,the factors influencing the spraying performance including downwash airflow field and operating parameters are analyzed,and a number of key technologies for reducing drift and enhancing the application efficiency such as remote sensing,variable-rate technologies,and spray drift models are considered.Based on the reviewed literature,future developments and the impacts of these UAV technologies are projected.This review may inspire the innovation of the combined use of big data analytics and UAV technology,precision agricultural spraying technology,drift reduction technology,swarm UAV cooperative technology,and other supporting technologies for UAV-based aerial spraying for scientific research in the world.

Analysis of variograms with various sample sizes from a multispectral image

Variogram plays a crucial role in remote sensing application and geostatistics.It is very important to estimate variogram reliably from sufficient data.In this study,the analysis of variograms computed on various sample sizes of remotely sensed data was conducted.A 100×100-pixel subset was chosen randomly from an aerial multispectral image which contains three wavebands,Green,Red and near-infrared(NIR).Green,Red,NIR and Normalized Difference Vegetation Index(NDVI)datasets were imported into R software for spatial analysis.Variograms of these four full image datasets and sub-samples with simple random sampling method were investigated.In this case,half size of the subset image data was enough to reliably estimate the variograms for NIR and Red wavebands.To map the variation on NDVI within the weed field,ground sampling interval should be smaller than 12 m.The information will be particularly important for Kriging and also give a good guide of field sampling on the weed field in the future study.