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.