Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops
It has been recognized for decades that low and inconsistent spray coverages of pesticide applications represent a major challenge to successful and sustainable crop protection. Deployment of water-sensitive spray cards combined with image analysis can provide valuable and quantitative insight into...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/11/10/916 |
| _version_ | 1797515624347336704 |
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| author | Christian Nansen Gabriel Del Villar Alexander Recalde Elvis Alvarado Krishna Chennapragada |
| author_facet | Christian Nansen Gabriel Del Villar Alexander Recalde Elvis Alvarado Krishna Chennapragada |
| author_sort | Christian Nansen |
| collection | DOAJ |
| description | It has been recognized for decades that low and inconsistent spray coverages of pesticide applications represent a major challenge to successful and sustainable crop protection. Deployment of water-sensitive spray cards combined with image analysis can provide valuable and quantitative insight into spray coverage. Herein we provide description of a novel and freely available smartphone app, “Smart Spray”, for both iOS and Android smart devices (iOS and Google app stores). More specifically, we provide a theoretical description of spray coverage, and we describe how Smart Spray and similar image-processing software packages can be used as decision support tools and quality control for pesticide spray applications. Performance assessment of the underlying pixel classification algorithm is presented, and we detail practical recommendations on how to use Smart Spray to maximize accuracy and consistency of spray coverage predictions. Smart Spray was developed as part of ongoing efforts to: (1) maximize the performance of pesticide sprays, (2) minimize pest-induced yield loss and to potentially reduce the amount of pesticide used, (2) reduce the risk of target pests developing pesticide resistance, (3) reduce the risk of spray drift, and (4) optimize spray application costs by introducing a quality control. |
| first_indexed | 2024-03-10T06:48:11Z |
| format | Article |
| id | doaj.art-64cad759d6844dafab267c6aade9a44c |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-10T06:48:11Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-64cad759d6844dafab267c6aade9a44c2023-11-22T17:04:10ZengMDPI AGAgriculture2077-04722021-09-01111091610.3390/agriculture11100916Phone App to Perform Quality Control of Pesticide Spray Applications in Field CropsChristian Nansen0Gabriel Del Villar1Alexander Recalde2Elvis Alvarado3Krishna Chennapragada4Department of Entomology and Nematology, University of California, Davis, CA 95616, USADepartment of Computer Science, University of California, Davis, CA 95616, USADepartment of Computer Science, University of California, Davis, CA 95616, USADepartment of Computer Science, University of California, Davis, CA 95616, USADepartment of Computer Science, University of California, Davis, CA 95616, USAIt has been recognized for decades that low and inconsistent spray coverages of pesticide applications represent a major challenge to successful and sustainable crop protection. Deployment of water-sensitive spray cards combined with image analysis can provide valuable and quantitative insight into spray coverage. Herein we provide description of a novel and freely available smartphone app, “Smart Spray”, for both iOS and Android smart devices (iOS and Google app stores). More specifically, we provide a theoretical description of spray coverage, and we describe how Smart Spray and similar image-processing software packages can be used as decision support tools and quality control for pesticide spray applications. Performance assessment of the underlying pixel classification algorithm is presented, and we detail practical recommendations on how to use Smart Spray to maximize accuracy and consistency of spray coverage predictions. Smart Spray was developed as part of ongoing efforts to: (1) maximize the performance of pesticide sprays, (2) minimize pest-induced yield loss and to potentially reduce the amount of pesticide used, (2) reduce the risk of target pests developing pesticide resistance, (3) reduce the risk of spray drift, and (4) optimize spray application costs by introducing a quality control.https://www.mdpi.com/2077-0472/11/10/916spray coveragedecision support toolspesticide applicationsspray performancesmartphone apps |
| spellingShingle | Christian Nansen Gabriel Del Villar Alexander Recalde Elvis Alvarado Krishna Chennapragada Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops Agriculture spray coverage decision support tools pesticide applications spray performance smartphone apps |
| title | Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops |
| title_full | Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops |
| title_fullStr | Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops |
| title_full_unstemmed | Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops |
| title_short | Phone App to Perform Quality Control of Pesticide Spray Applications in Field Crops |
| title_sort | phone app to perform quality control of pesticide spray applications in field crops |
| topic | spray coverage decision support tools pesticide applications spray performance smartphone apps |
| url | https://www.mdpi.com/2077-0472/11/10/916 |
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