Determining the Effectiveness of Soil Treatment on Plant Stress using Smart-Phone Cameras

Abstract

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this paper, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficiencies in soil using smart-phone cameras, coupled with image processing and machine learning algorithms. The greenhouse experiment was conducted by growing two plant species; willows (Salix Pentandra) and poplars (Populus deltoides x nigra, DN34), in two treatments. These treatments included: unamended tailings (collected from a lead mine tailings pond and characterized by nutrient deficiency), and biosolids amended tailings. Biosolids are very rich in nutrients and were added to the tailings in one of the two treatments to supply plants with nutrients. Subsequently, we captured various images of plant leaves grown in both soils. Each image taken was pre-processed via filtration to remove associated noise, and was segmented into pixels to facilitate scalability of analysis. Subsequently, we designed random forests based algorithms to detect the stress of leaves as indicated by their coloring. In a dataset consisting of 34 leaves, our technique yields classifications with a high degree of prediction, recall and F1 score. Our work in this paper, while restricted to two types of plants and soils, can be generalized. We see applications in the emerging area of urban farming in terms of empowering citizens with tools and technologies for enhancing quality of farming practices.

Meeting Name

2016 International Conference on Selected Topics in Mobile and Wireless Networking, MoWNeT 2016 (2016: Apr. 11-13, Cairo, Egypt)

Department(s)

Computer Science

Second Department

Civil, Architectural and Environmental Engineering

Keywords and Phrases

Artificial intelligence; Biological materials; Biosolids; Cameras; Classification (of information); Cotton; Decision trees; Greenhouses; Image processing; Learning algorithms; Learning systems; Nutrients; Smartphones; Soils; Tailings; Telephone sets; Farming practices; Greenhouse experiments; Nutrient deficiency; Participatory Sensing; Plant stress; Populus deltoides; Smart-phone cameras; Tools and technologies; Plants (botany); Machine learning algorithms; Smart-phones

International Standard Book Number (ISBN)

978-1-5090-1743-0

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Apr 2016

Link to Full Text

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