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Infestation caused by stalk rot diseases is a common and severe threat to corn plantations. Since this damage often occurs internally within the corn stalk, detecting its extent without cutting open the stalk is challenging. Even the best statistical test designs can lead to significant plant loss, as many non-infested plants may need to be sacrificed. A better solution is to implement a nondestructive testing regimen implemented on an edge device, such as the arm of a perambulating robot. This robot could perform instant tests on individual plants, providing information on the volume of infestation damage without causing harm. In this work, we lay the foundation for such a test. We know from the physics of charge flow that electric field lines flowing through a conducting body tend to avoid any ingrained vacuums (or air pockets), on account of the markedly lower conductivity of the vacuum compared to the conducting material. The famous Calderon's problem exploits this principle and recommends a framework to solve such detection problems. Our proposed test builds on Calderon's idea, combining it with ideas from statistical sampling theory and and principles of machine learning, to predict the volumetric extent of damage caused by stalk rot diseases with very high accuracy ($R^2 \approx 0.99$). The key highlights of our test design are its low instrumentation cost, speed, and high reliability in assessing the volumetric damage.
Dataset Information:
```
We expose all the data in one convenient csv file, "MLSet.csv". This dataset can be imported directly as a pandas dataframe.
```
License
CC-BY-NC-4.0
Downloading dataset from Huggingface
To run the example code, you need to install the following package:
```
pip install huggingface_hub
```
The following script demonstrates how to download a directory from the Hugging Face Hub:
```
from huggingface_hub import HfApi, hf_hub_download
import os
import shutil
REPO_ID = "BGLab/calderon"
DIRECTORY = "poisson"
# Initialize the Hugging Face API
api = HfApi()
# List files in the directory
files_list = api.list_repo_files(repo_id=REPO_ID, repo_type="dataset")
# Filter the files in the specified directory
files_to_download = [f for f in files_list if f.startswith(DIRECTORY)]
# Create local directory if it doesn't exist
os.makedirs(DIRECTORY, exist_ok=True)
# Download each file
for file in files_to_download:
file_path = hf_hub_download(repo_id=REPO_ID, filename=file, repo_type="dataset")
# Copy the file to the local directory using shutil.copy2
shutil.copy2(file_path, os.path.join(DIRECTORY, os.path.basename(file_path)))
print("Files downloaded successfully.")
```
Directory Structure