Back to Search Start Over

Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence

Authors :
Davide Facchinetti
Stefano Santoro
Lavinia Eleonora Galli
Giulio Fontana
Lorenzo Fedeli
Simone Parisi
Luigi Bono Bonacchi
Stefan Šušnjar
Fabio Salvai
Gabriele Coppola
Matteo Matteucci
Domenico Pessina
Source :
Applied Sciences, Vol 11, Iss 5, p 1992 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named “rover”), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.3a544a47a9594c9da85f15fcf220d7b4
Document Type :
article
Full Text :
https://doi.org/10.3390/app11051992