Soil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sensors and sensing technology combined with traditionally collected minimum information of soil show strong promise in developing soil suitability protocol for crops. This case study aims to leverage digital soil sensing and mapping technology to establish a relationship with farm-level crop suitability decisions developed over decades of trial and error and good practices. The Hipple Farm (150 acres), established in 1812 and located in Niagara regions in Ontario, Canada is a mixed fruit farm and the crops are tailored to the soils. For example, on heavier soils, they grow grapes and European blue plums; medium soils are pears and apples; and sandy soils are peach, apricot, nectarine, and cherry trees. The crop choices are based on decades of trial and error of practices and experiences of the owner, who lived on the farm for generations. The layout and maps of crops were collected from the owner, geolocated, and verified with an areal image collected using an unmanned areal vehicle. Environmental covariates including DEM and its derivatives, geology, climate, remote sensing data were collected from various sources. The farm was mapped with a DUALEM and RTK at multiple depths. All this information was then used to identify sampling locations following conditional Latin Hypercube sampling design and soil core samples were collected down about 1.1 to 1.2 m depth. Soil texture, bulk density, organic carbon, pH, and electrical conductivity were measured in the laboratory for each horizon identified following the Canadian System of Soil Classification. Environmental covariates, soil sensor data, and laboratory-measured data were used to develop digital maps of soil properties. All this information was further used to develop the clustered area (using k-means clustering) with similar characteristics or management zones. These were then compared with the layout of the crops and current practices. A simple overlapping of the geolocated crop layout and sensor information-driven management zones showed strong similarity. Statistical analysis showed strong spatial similarity between these two layers of information. The differences in soil characteristics of management zones were associated with crops currently being grown and selected based on decades of experience (information collected using interviews with the owner). Although the spatial crop distribution of this farm was generated through decades of farming experience, proximal sensing can provide detailed soil property information very quickly. This information can be used to make smart, cost-saving management decisions.