Evidence map›Paper›PMID 41744386›Full record

ArticleScience progress

A method for locating soil monitoring points in greenhouse cultivation based on DeeplabV3.

Yi Lu, Jiayu Chen, Bin Wu, Ying Liu, Kezheng Sun, Di Jiang, Yang Yi, Chenlong Fan, Mengmeng Qiao

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Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yi LuCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Jiayu ChenCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.ORCID 0009-0001-6773-0791
Bin WuCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Ying LiuCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Kezheng SunCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Di JiangCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Yang YiCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Chenlong FanCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
Mengmeng QiaoCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate positioning of water and fertilizer monitoring points in the soil of potted plants contributes to improving the level of precision agriculture management. This study proposes an automated extraction method for water and fertilizer detection points in blueberry potted plants in a greenhouse scenario. Based on the improved DeeplabV3+, a lightweight soil region segmentation network (LSRS-Net) is proposed. Firstly, the original DeeplabV3 + backbone was replaced by the lightweight MobileNetV2 to reduce computational complexity. Then, the Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) module enhances multi-scale context feature fusion by embedding a Feature Pyramid Network (FPN) between the encoder and decoder to achieve cross-layer semantic fusion. In addition, the optimization algorithm based on Euclidean distance has improved the coordinates of the detection points. The experimental results show that, compared with the original model, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) of the LSRS-Net model have increased by 2.98% and 3.84%, respectively, and the processing speed has reached 25.24 ms per frame. The extraction validity rate (EVR) exceeds 98.33%, which meets the requirements for fast and stable application in greenhouse cultivation and provides a viable solution for intelligent water and fertilizer management in agriculture.

Indexed as

Automatic inspectionDeeplabV3+localizationmachine visionwater and fertilizer monitoring

Identifiers

PMID41744386
PMCPMC12949284

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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.