Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning.

Abstract

Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R 2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R 2 of 0.71. The overall performance in both countries reached an R 2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.

Plant phenotyping relevance

スマートフォン画像と深層学習でコーヒー果実数を推定する方法を開発・検証しており、植物形質の取得が研究の中心です。

Code and data availability

The paper's Data Availability statement explicitly deposits representative coffee cherry pictures (phenotyping image data) and the authors' Python analysis script in a public GitHub repository, matching the paper's smartphone-image cherry counting analysis. Other URLs (FAOSTAT, SENAMHI, IDEAM, Label Studio, YOLOv5 docs