This study explores a field-deployable soil texture classification method based on mobile images. Soil texture is one of the fundamental elements of a successful landscape project. It impacts plants’ root growth, drainage, moisture balance, and the overall resilience of an ecosystem, making it a core metric of planting success, stormwater function, and long-term sustainability. Conventional texture analysis, however, relies on laboratory equipment and sampling procedures that are time-consuming and costly. This study explores a Soil Texture Recognition Platform (STRP) based on mobile phone images to quickly identify texture types on real soil surfaces without laboratory processing. It employs Convolutional Neural Network, texture feature extraction, and supervised learning model training. Through a comparison experiment based on manual and platform-based evaluation, the researcher assesses and further improves this STRP’s effectiveness and accuracy. This research provides the landscape architecture field with a low-cost, reproducible, and rapid method for soil texture testing, contributing to evidence-based design and smart landscapes.