Evaluating Model and BeiDou Based Management System for Scale Operation of Cotton-Pickers

For scale cotton-picker operation, combination of production resources including field, machine, and drivers, should be organized reasonably both in temporal and spatial dimensions. Xinjian Agri. is such a scale cotton picking service company, which owns more than 400 cotton-pickers, hires nearly 1000 personnel, and works for more than ten big farms each season. The total operation area is about 90,000 ha. In this paper, a Cotton-picker Operation Scheduling & Monitoring System (CPOSMS) was developed for Xinjian Agri. CPOSMS is a WebGIS and BeiDou based management software, which includes four main function modules. Overall scheduling module aims to help the company to create machine fleets for the farms based on operation demands and operation capacity. A real-time evaluation model was studied to adjust the rationality. Local scheduling module is to dispatch machines and personnel to form machine unit. Central navigating module is to guide staff to specific field. Operation monitoring module is to monitor and analyze operation process. Experiments in 2015 showed that the CPOSMS is the necessary tool for the company, and the evaluation model and BeiDou based system can improve management efficiency.

Behavior modelling and sensing for machinery operations using smartphone’s sensor data:A case study of forage maize sowing

Large-scale agricultural machinery cooperatives require technical statistic report of agricultural machinery operations to improve the efficiency of fleet management.This research proposed a smartphone-based solution to build the behavior model for agricultural machinery operations by using the embedded sensors including the GNSS,the accelerometer,and the microphone.The whole working process of agricultural machinery operation was divided into four stages:preparation,operation,U-turn,and transfer,each of which may contain the behaviors of stalling and idling.Field experiments were carried out by skilled operators,whose operations were typical agricultural machinery operations that could be used to extract behavior features.Butterworth low-pass filter was used to smooth the output from the accelerometer.Then,the operating data were collected through an APP when sowing the forage maize as a case study.Four stages of machinery operation can be preliminarily classified by using GNSS speed,while the identification of behaviors such as sudden acceleration and longtime idling that may increase fuel consumption,reduce machinery life,or decrease the working efficiency,requires extra information such as acceleration and sound intensity.The results showed that the jerk of accelerating can describe the severity of the sudden acceleration,the standard deviation of forward acceleration can reflect the smoothness of operation,the upward acceleration can be used to identify behaviors of stalling and idling,and the sound intensity during idling can capture the behavior of goosing the throttle.Further,the operating behavior figure can be drawn based on the above parameters.In conclusion,this research constructed several behavior models of agricultural machinery and operators by using smartphone’s sensor data and established the base of the online assessing and scoring system for agricultural machinery operations.

Classification and evaluation of uncertain influence factors for farm machinery service

Uncertainty extremely interferes with the execution of farm machinery operation.Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors(UIFs)than family farms.Under social service circumstance,uncertainties may arise from participants and environments.Classification and evaluation of UIFs were studied in this research.According to the production system,32 UIFs are defined and classified into six categories,which include supply,demand,interactivity,nature,society and others.Uncertainty composite index(UCI)is defined to evaluate the importance of UIFs,which is the square root of the product of occurrence frequency(OF)and impact degree(ID)calculated from the well-designed questionnaire responded by farm machinery operators.UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance.Results from questionnaire showed that natural UIFs have an extreme impact on farm operation,UIFs of the demand and the supply have a serious influence on farm operation,UIFs of interactivity cannot be ignored,and social UIFs have a weak impact on farm operations.This study discovered the uncertainty problems under the specific circumstance of farm machinery service,which may provide a theoretical basis and potential methods for risk management of machinery cooperatives.

Smartphone based precise monitoring method for farm operation

Although Global Navigation Satellite System(GNSS)terminal has been widely used for fleet management,it cannot satisfy the need of managing changes of laborer and implement during farm operation,which are important for social service cooperatives comparing to family farms in China.The objective of this study was to explore a precise,low cost and easy-to-use method for vehicle fleet management of large scale farm machinery cooperatives.A smartphone based application software(APP)named Precise Monitoring System(PMS)was developed to record the farm operation information including tractor,implement and laborer by scanning their Quick Response codes(QR codes),and obtain real time GNSS positions by using built-in GNSS chip of smartphone.Considering the convenience usage for farmers,only two buttons,“start/pause/continue”and“stop”were designed to record farm operation status.Finally,IDs,positions and operation status were combined and transferred to the server through GPRS/3G/4G.Two kinds of experiments were designed and conducted to verify the PMS.The results showed that PMS can realize the basic functions such as precise and real-time monitoring,operation quality tracing,operation mileage and operation area calculating,and U-turn processing.The method and APP could record complex combination of production factors precisely and accurately,which is suitable for the management of vehicle fleet,and can replace GNSS terminal to some extent.

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.