Tillage is a primary agricultural task that causes progressive soil movement and, consequently, severe erosion in sloping farmland, with a high impact on crop productivity, soil quality and landscape features. Accordingly, this research combined aerial images taken with unmanned aerial vehicles and object-based image analysis (OBIA) to develop an innovative OBIA4tillage procedure with three main objectives: (i) analysing plowed agricultural fields, identifying and mapping the tillage marks, and automatically computing the main direction of the tillage furrows, (ii) validating the procedure quality in different scenarios by evaluating the accuracy of the results as affected by the sensor used (visible-light vs. multispectral), background soil hue, and ground vegetation density; and (iii) mapping contour farming and non-contour farming areas as indicators of potential low and high soil erosion risk, respectively. Twenty olive parcels from two different locations with a wide range of tree sizes, soil hue, parcel shapes and land slopes were selected as model systems to develop and validate the procedure. The OBIA4tillage procedure produced tillage maps with very high accuracy for both RGB and multispectral images (R2 of 0.99 and 0.93, respectively), as obtained from the linear equation between estimated and groundtruth values. The results were similar in clear and dark soils (R2 of 0.96 in both cases), although there were notable differences between areas of dense ground vegetation or bare soil (R2 of 0.99 in both cases) and areas of medium vegetation cover (R2 of 0.81). The application of contour farming in the study region was moderate at location 1 (42.35% of the study area) but more widespread at location 2 (72.60% of the study area), which revealed the uneven involvement of the local farmers in the challenge of controlling soil erosion risks.
