Evidence map›Paper›PMID 34066785›Full record

ReviewSensors (Basel, Switzerland)2021

Opportunities and Possibilities of Developing an Advanced Precision Spraying System for Tree Fruits.

Md Sultan Mahmud, Azlan Zahid, Long He, Phillip Martin

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Md Sultan MahmudDepartment of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0002-8141-105X
Azlan ZahidDepartment of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0001-6202-8680
Long HeDepartment of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
Phillip MartinFruit Research and Extension Center, The Pennsylvania State University, Biglerville, PA 17307, USA.ORCID 0000-0002-0967-826X

Funding

National Institute of Food and Agriculture 2019-70006-30440National Institute of Food and Agriculture PEN04653, Accession No. 1016510Northeast SARE GNE20-234-34268
6 · The paper itself

Abstract

Reducing risk from pesticide applications has been gaining serious attention in the last few decades due to the significant damage to human health, environment, and ecosystems. Pesticide applications are an essential part of current agriculture, enhancing cultivated crop productivity and quality and preventing losses of up to 45% of the world food supply. However, inappropriate and excessive use of pesticides is a major rising concern. Precision spraying addresses these concerns by precisely and efficiently applying pesticides to the target area and substantially reducing pesticide usage while maintaining efficacy at preventing crop losses. This review provides a systematic summary of current technologies used for precision spraying in tree fruits and highlights their potential, briefly discusses factors affecting spraying parameters, and concludes with possible solutions to reduce excessive agrochemical uses. We conclude there is a critical need for appropriate sensing techniques that can accurately detect the target. In addition, air jet velocity, travel speed, wind speed and direction, droplet size, and canopy characteristics need to be considered for successful droplet deposition by the spraying system. Assessment of terrain is important when field elevation has significant variability. Control of airflow during spraying is another important parameter that needs to be considered. Incorporation of these variables in precision spraying systems will optimize spray decisions and help reduce excessive agrochemical applications.

Indexed as

canopy densitycanopy detectioncanopy volumecrop protectiondeep learningmachine visionsensing

Identifiers

PMID34066785
PMCPMC8125941

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.