TY - JOUR
T1 - Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals
AU - Farooque, Aitazaz A.
AU - Hussain, Nazar
AU - Schumann, Arnold W.
AU - Abbas, Farhat
AU - Afzaal, Hassan
AU - McKenzie-Gopsill, Andrew
AU - Esau, Travis
AU - Zaman, Qamar
AU - Wang, Xander
N1 - Publisher Copyright:
© 2022
PY - 2023/2
Y1 - 2023/2
N2 - The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb's quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.
AB - The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb's quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.
KW - Automation
KW - Decision support system
KW - Precision agriculture
KW - Resource management
KW - Technology development
UR - http://www.scopus.com/inward/record.url?scp=85138701375&partnerID=8YFLogxK
U2 - 10.1016/j.atech.2022.100073
DO - 10.1016/j.atech.2022.100073
M3 - Article
AN - SCOPUS:85138701375
SN - 2772-3755
VL - 3
JO - Smart Agricultural Technology
JF - Smart Agricultural Technology
M1 - 100073
ER -