Abstract:
Accurate crop discrimination is vital for effective agricultural planning and sustainability management, especially in regions like SubSaharan Africa (SSA), where small-scale farming predominates and
ground data is scarce. Conducting field surveys in SSA is challenging due to labor and cost constraints, as well as logistical and
political barriers. This paper explores the feasibility of transferring
crop-type classification models between regions with similar crops.
Utilizing data from Baraouéli and Karamoja, collected from Source
Cooperative, we trained multi-layer perceptron (MLP), random
forest (RF), and support vector machine (SVM) classifiers using
Sentinel-2 imagery. These models were then evaluated and applied to cross-crop type classification between Baraouéli (Mali) and
Karamoja (Uganda) to assess the transferability of machine learning
models. While the models demonstrated strong local performance,
achieving high overall accuracy in their respective regions, their
performance declined when transferred between regions. However, focusing solely on specific crops such as maize and sorghum
improved the models’ performance, albeit with reduced accuracy
compared to local classifications. The study suggests that incorporating additional features such as texture, DEM, crop height, and
weather data could enhance the adaptability of classifiers between
regions. These findings highlight the potential for developing transferable models within SSA to address challenges related to limited
ground surveyed data, providing valuable insights for researchers
and policymakers
