Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory research.In the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was proposed.First,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory data.Second,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature maps.Then,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory point.To validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural trajectories.On the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

An integrated foot transducer and data logging system for dynamic assessment of lower limb exerted forces during agricultural machinery operations

Agricultural machinery typically requires lower limb actuation forces for operations such as treadling,pedaling and tractor based.However,limited systems exist for assessment of such forces that have ergonomic influence.This study,therefore developed and evaluated a single board computer integrated foot transducer(IFT)and autonomous data logging and visualization systemtomonitor dynamic lower limb exerted forces.The systemconsists of custom developed load sensors sandwiched into foot shaped units that fit operator's both feet.Stamped forces at crank angles for operations typical to pedaling while at height(above ground level)for operation representing typical treadling operations were recorded on-board amemory card and displayed on a liquid crystal display.Evaluations were conducted by imposing external loads that significantly increased(p b 0.05)the foot exerted forces.Force trends were periodic with peaks of 73,85,110.5 and 145.4 N for left foot and 41,50,131.7 and 145.4 N for right foot at loads of 10,30,50 and 70 N,respectively during pedaling operations.Similarly,the left lower actuation limb exerted forces of 139,249 and 255 N at 5,10 and 15 N of imposed loads,respectively during treadling operation.System was also evaluated for tractor operations and exerted forces ranged from 92 to 164 and 107–176 N for clutch pedal engagement at lower to higher tractor speeds on farm and tarmacadam roads,respectively.Similarly,for brake pedal engagement,such forces ranged from106 to 173 and 120–204 N on farm and tarmacadamroads.These forces varied significantly at different forward speeds.Results suggest potential of such system for foot exerted force assessments typical to agricultural machinery systems in real field.Designsmay be evaluated or reconsidered tominimizemusculoskeletal disorder risks during prolonged operations.Work-rest schedules protocols can be developed by ergonomists for safe,efficient and comfortable operations.

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.