Design and Development of a Smart Variable Rate Sprayer Using Deep Learning

Nazar Hussain,Andrew Mckenzie-Gopsill,Arnold Schumann,Farhat Abbas,Travis Esau,Bishnu Acharya,Aitazaz Farooque,Qamar Zaman

doi:10.3390/rs12244091

Abstract

The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.

Highlights

Site-specific variable rate application (VA) of herbicides is preferred over uniform application (UA) to kill weeds and/or disease infections present in patches in agricultural fields
Since the inference speed of tiny-YOLOv3 was considerably higher than the speed of YOLOv3, due to the high value of FPS, the tiny-YOLOv3 model was finalized to deploy in the smart variable rate sprayer (SVRS)
The study results showed that the mAP value for the weed dataset was comparatively higher than for the simulated disease plants dataset

Summary

Introduction

Site-specific variable rate application (VA) of herbicides is preferred over uniform application (UA) to kill weeds and/or disease infections present in patches in agricultural fields. There are different spaying methods for agrochemical field applications, including broadcasting and band applications. The former is inefficient as it uniformly applies chemicals in agricultural fields without considering the variable conditions of the field. By using this technique, around 60–70% of spraying chemicals are wasted [8]. Almost 40% of herbicides can be saved by considering the patches of weeds and/or disease infections in the field during the spraying operation. For bushes and tree crops, up to 75% of pesticides can be saved by variably applying the chemicals according to the canopy structure of trees [9]

Methods

Results

Discussion

Conclusion