In recent years, the agricultural sector has seen significant techno- logical improvements under the flag of precision agriculture, assisting farmers in the manageability that coincides with large-scale farming. Moreover, precision agriculture aims to enable plant-specific farming on the macro scale that is demanded by the current global population growth. By more closely matching the individual needs of the plants, farmers are able to increase crop yield while reducing the environmen- tal footprint as well as the economic cost of farming due to savings in fertilizers and pesticides. Visual inspection of arable land is a key factor in maintaining trace- ability of plant growth and health in precision farming. More specif- ically, plant counting, size measurement and plant localisation are of great use for farmers in yield prediction, growth tracking, and obtain- ing insight in the emergence ratio of the crop. Most of the state-of-the-art plant counters, or object counters in gen- eral, rely on human annotations (labour intensive and error prone) as exemplars for the counting model. Self-supervised object counting, however, is a machine learning paradigm independent of human la- belled data, enabling an object counter to learn solely from raw photo- graphic data. Furthermore, the generative character of self-supervised learning models implies the potential to generalize well on unseen data to the model, such as new plant species or variations in plant size in the case of plant counting. In this master’s thesis in cooperation with Tective Robotics, a study is performed towards the design of self-calibration based self-supervised object counting and localisation model for the scenario of early crop stand scenarios. More specifically, a novel self-calibrator has been developed to estimate the planting distance in between crops and a threshold for small object noise filtering (weeds, loose leaves ect). Im- plications of the self-calibrator are robustness to variations in plant size and allignement, accurate segmentation of occluded plant clusters and small-sized weed suppression. Model testing on UAV orthomosaic arable land imagery collected by Tective Robotics B.V. has shown outstanding performance (R2 = 0.94) of the newly developed plant counting model, without the need of any labelled training data. The plant counter is comparable in performance to some of the commercially available plant counters. The plant counter, embedded in the entire data processing pipeline for Geotiff orthomosaics, has been made available on GitHub.