TY - GEN A1 - Rivera-Palacio, Juan C. A1 - Bunn, Christian A1 - Ryo, Masahiro T1 - Factors affecting deep learning model performance in citizen science–based image data collection for agriculture : a case study on coffee crops T2 - Computers and Electronics in Agriculture N2 - Citizen science is an effective approach for collecting extensive data scalable for deep learning, although data quality is debatable. However, few studies have determined the factors associated with data collection that affect model performance and potential sampling bias. This study aims to identify the factors that significantly influence the performance of a deep learning object detection model in agricultural prediction tasks. To do so, we analyzed errors in a You Only Look Once (YOLO v8) model trained for counting the number of coffee cherries in mobile pictures. The model was trained with 436 images taken in Colombia and Peru collected by local farmers as a citizen science approach. We analyzed the prediction errors of the model using 637 additional pictures. We then applied a linear mixed model (LMM) and a decision tree machine learning model to regress the model’s error against predictor variables related to the following categories: photographer influence, geographic location, mobile phone characteristics, picture characteristics, and coffee varieties. Our results show the strong influence of photographer identity and adherence (whether the image collection protocol was followed or not) on model prediction error. Following the protocol can increase model performance from an R² of 0.48 to 0.73. Additionally, model performance varied significantly depending on photographer identity, with R² ranging from 0.45 to 0.93. In contrast, factors such as mobile phone characteristics (e.g., frontal camera resolution, flash type, and screen size), using the screen behind the branch to obscure other cherries, coffee varieties, and geographic location did not significantly affect prediction error. These findings demonstrate that data quality in citizen science–based data collection for enhancing model prediction can be achieved through straightforward and comprehensive protocols, customized volunteer training, and regular feedback from experts. Such measures collectively support the robust application of deep learning models in agriculture. Furthermore, this study demonstrated that any mobile device with a camera can contribute to citizen science initiatives, underscoring the potential and scalability of this approach in agricultural research. KW - Deep learning KW - Bias KW - Citizen science KW - Smartphone imaging KW - Coffee KW - Prediction Y1 - 2025 U6 - https://doi.org/10.1016/j.compag.2025.110096 SN - 0168-1699 VL - 232 SP - 1 EP - 13 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Rivera‐Palacio, Juan C. A1 - Bunn, Christian A1 - Ryo, Masahiro T1 - Smartphone‐based monitoring identifies the importance of farm size and soil type for coffee tree productivity at a large geographic scale T2 - Journal of sustainable agriculture and environment N2 - Smartphone‐based monitoring has been increasingly applied to coffee crops for multiple tasks, such as predicting coffee tree productivity. However, its implementation remains limited to small‐scale use, typically at the individual plant level. At larger scales, such as the farm level, its application is largely unexplored. Moreover, it is unclear whether the use of smartphone‐based monitoring can help identifying key factors driving coffee tree productivity such as climate, soil, and management characteristics. To address these challenges, we investigate coffee tree productivity at the farm level and its key driving factors using smartphone‐based monitoring and explainable artificial intelligence (xAI), and compare the results with those obtained from manual monitoring at the farm level. We used a multimodal data set composed of satellite data (soil and climate), smartphone‐based monitoring (coffee tree productivity), and management characteristics (area, shade trees, and farm shape). The results showed that smartphone‐based monitoring reached a of R ² = 0.84 in predicting coffee tree productivity at the farm level. The xAI results revealed that both smartphone‐based and manual monitoring approaches identified the coffee cultivation area (greater than 13 ha) and soil texture (sandy, clay loam) as the most important variables influencing coffee tree productivity at farm level. The analysis also indicated that shade trees do not significantly affect coffee tree productivity. These findings suggest that smartphone‐based monitoring can serve as a reliable and scalable alternative to manual monitoring for evaluating coffee tree productivity at the farm level. KW - Citizen science KW - Coffee KW - Explainable artificial intelligence KW - Monitoring KW - Soil Y1 - 2025 U6 - https://doi.org/10.1002/sae2.70111 SN - 2767-035X VL - 4 IS - 4 SP - 1 EP - 9 PB - Wiley CY - Hoboken, NJ ER -