Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications.

ABSTRACT: Conventional methods of crop yield estimation are costly, inefficient, and prone to error resulting in poor yield estimates. This affects the ability of farmers to appropriately plan and manage their crop production pipelines and market processes. There is therefore a need to develop automated methods of crop yield estimation. However, the development of accurate machine-learning methods for crop yield estimation depends on the availability of appropriate datasets. There is a lack of such datasets, especially in sub-Saharan Africa. We present curated image datasets of coffee and cashew nuts acquired in Uganda during two crop harvest seasons. The datasets were collected over nine months, from September 2022 to May 2023. The data was collected using a high-resolution camera mounted on an Unm

SUBMITTER: Sanya R 

PROVIDER: S-EPMC10788206 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature