Abstract
Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area.
Highlights
Maize, and legumes, which are key to food security in Rwanda
While most works in the literature using unmanned aerial vehicles (UAVs) in smallholder agriculture focus on a single crop type, this study modeled six common classes of land cover to help better understand the feasibility of a more comprehensive, high-resolution crop mapping for East African smallholder systems
Our objective is to better understand the promise and challenges of UAV agricultural classification methods in settings vastly different from large monocrop plots commonly adopted in industrial agriculture
Summary
Achieving food security for a growing global population will require significant advances in local capacity, market building, and technology. An important component of improving food security in the near term is better information on seasonal agricultural production, made available as early as possible during the growing season and updated as conditions change [1]. Having timely access to information on crop progress by area can aid in the logistics of harvesting, processing, and marketing crops. Identifying regions where agricultural planting is delayed, or crop development is behind schedule, can help inform allocation of resources and improve preparation for mitigating food insecurity in those regions [2]. In many regions of the world, agricultural data lack the accuracy, centralization, structure, and consistency for farmers and government stakeholders to make timely decisions [3]
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