Data for PhenoCam Images Raspberry Pi Models for Corn Growth Stage Classification

Published: 26 June 2026| Version 1 | DOI: 10.17632/vp5rrs62zn.1
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Description

This dataset on “Data for PhenoCam images Raspberry Pi models for corn growth stage classification” includes the original data, intermediate model outputs, and final results obtained (graphical and tabular forms) from the research study. The original data (images) consists of 10 different corn growing PhenoCam sites in US, which were annotated for growth stage labeling manually based on visual appearance. The intermediate model output includes learning curves (training and validation log), and predictions of test images, for respective deep learning (DL) models (4) across individual PhenoCam sites (10) and image clipping levels (5). The final results after model development and testing comprise confusion matrices and classification reports. The lightweight DL models developed for this study are ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2 using transfer learning techniques. The results are based on site-wise and five PhenoCam image clipping levels (0-40%) across individual models. The dataset provides model performance evaluation results based on intrasite (training and testing on the same PhenoCam site) and intersite (testing on a new PhenoCam site) methods. Initially, models were developed using a supercomputer (CCAST, NDSU), and they were finally deployed on Raspberry Pi (single board computer). The dataset documents the implementation and performance of the finalized model (ELiteCrop0) deployed on Raspberry Pi, including inference time, computational efficiency, and hardware utilization metrics. The contents of the dataset include: 1. Abstract, 2. PhenoCam data annotation visual class labels, 3. Training and validation accuracy and loss curves, 4. Confusion matrix plots, 5. Sample prediction plots, 6. Classification report, 7. CPU timing, 8. Sample prediction plots for Raspberry Pi, 9. Intersite evaluation with ELiteCrop0, and 10. Intersite sample prediction plots for Raspberry Pi with ELiteCrop0.

Files

Steps to reproduce

• Download PhenoCam images from the “PhenoCam Network” (an open-source cloud platform). • The downloaded images will be in the month-wise corresponding folders (e.g. May to October). • Manually annotate images into new folders named based on visual appearance and respective corn growth stages. • Incorporate PhenoCam images to develop lightweight DL models using the transfer learning technique, then train and validate the models. • In this study, the overall model development process was performed on a supercomputer (CCAST, NDSU). • Once the model is trained, it’s ready to predict the test images and generate a confusion matrix and classification reports accordingly. • Optimize the developed model into a Raspberry Pi deployable lite version using the TFLite interpreter. • For the Raspberry Pi-based corn growth stage classification, load test images into the Raspberry Pi and write an inference script to predict the PhenoCam images.

Institutions

Categories

Agricultural Engineering, Computer Model Development, Machine Vision, Precision Agriculture, Phenology, Deep Learning, Edge Computing

Funders

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