ArticleHeliyon2021
Soil color analysis based on a RGB camera and an artificial neural network towards smart irrigation: A pilot study.
Article in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- IrriDrone: Overhead drone-based video dataset for precision sprinkler irrigation assessment.Data in brief · 2026Article
- Soil Region Segmentation and Visual Whiteness Analysis in Cold-Region Rice Seedbeds Based on Improved DAC-UNet.Plants (Basel, Switzerland) · 2026Article
- Article
- Predicting wheat yield using deep learning and multi-source environmental data.Scientific reports · 2025Article
- Low-Cost Optical Sensors for Soil Composition Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Irrigation operations in agriculture are one of the largest water consumers in the world, and it has been increasing due to rising population and consequent increased demand for food. The development of advanced irrigation technologies based on modern techniques is of utmost necessity to ensure efficient use of water. Smart irrigation based on computer vision could help in achieving optimum water-utilization in agriculture using a highly available digital technology. This paper presents a non-contact vision system based on a standard video camera to predict the irrigation requirements for loam soils using a feed-forward back propagation neural network. The study relies on analyzing the differences in soil color captured by a video camera at different distances, times and illumination levels obtained from loam soil over four weeks of data acquisition. The proposed system used this color information as input to an artificial neural network (ANN) system to make a decision as to whether to irrigate the soil or not. The proposed system was very accurate, achieving a mean square error (MSE) of 1.616 × 10
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Registered trials
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