Recognition of cotton growth period for precise spraying based on convolution neural network

Dynamic acquisition of crop morphology is beneficial to real-time variable decision of precise spraying operations in fields.However,the existing spraying quantity regulation has high tolerance on the statistical characteristics of regional morphology,so expensive LiDAR and ultrasonic radar can’t make full use of their high accuracy,and can reduce decision speed because of too much detail of branches and leaves.Therefore,designing a novel recognition system embedded machine learning with low-cost monocular vision is more feasible,especially in China,where the agricultural implements are medium sizes and cost-sensitive.In addition,we found that the growth period of crops is an important reference index for guiding spraying.So,taking cotton as a case study,a cotton morphology acquisition by a single camera is established,and a cotton growth period recognition algorithm based on Convolution Neural Network(CNN)is proposed in this paper.Through the optimization process based on confusion matrix and recognition efficiency,an optimized CNN model structure is determined from 9 different model structures,and its reliability was verified by changing training sets and test sets many times based on the idea of kfold test.The accuracy,precision,recall,F1-score and recognition speed of this CNN model are 93.27%,95.39%,94.31%,94.76%and 71.46 ms per image,respectively.In addition,compared with the performance of VGG16 and AlexNet,the convolution neural network model proposed in this paper has better performance.Finally,in order to verify the reliability of the designed recognition system and the feasibility of the spray decision-making algorithm based on CNN,spraying deposition experiments were carried out with 3 different growthperiods of cotton.The experiments’results validate that after the optimal spray parameters were applied at different growth periods respectively,the average optimum index in 3 growth periods was 42.29%,which was increased up to 62.24%than the operations without distinguishing growth periods.

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.

Precision orchard sprayer based on automatically infrared target detecting and electrostatic spraying techniques

There is an urgent need for new chemical application techniques and sprayers in Chinese orchard spraying.A new tractor-mounted automatic target detecting electrostatics,and air-assisted orchard sprayer was designed and developed to meet the demand of chemical pest control in orchards.This sprayer light weighted,highly efficient,reduces pesticide use and is friendly to the environment.The techniques of automatic target detecting,electrostatics,and air-assisted spraying were combined in this system.The electrostatically charged droplets are projected toward the target by the assistance of an air stream that increases the droplets penetration within canopy.Experimental results show that the new automatic target detecting orchard sprayer with an infrared sensor can save more than 50%to 75%of pesticides,improve the utilization rate(over 55%),control efficiency,and significantly reduce environmental pollution caused by the pesticide application.At the same time the key technological problems related to air-assisted low volume and electrostatic spraying were solved.