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

摘要

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 thesis, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficienceies in soil using smartphone 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 filteration 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 thesis, 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.