Dear Colleagues,

The new term in agriculture is ‘Digital Agriculture’. Data from traditional sampling in production fields are being saved in digital formats. Real-time field data collected by sensors is being saved in computer devices. Scouting data during the growing season is being uploaded to cloud servers directly from fields. Planting and harvesting data are being transferred to home computers and are uploaded onto cloud servers immediately. Digital platforms for agriculture gather data from producers, analyze accumulated data, display results, and give recommendations of better managements next year. Many producers and agronomists are using digital agriculture platforms not just for precision agriculture practices but for all kinds of field management. So, ‘Digital Agriculture’ has become a broader term than ‘Precision Agriculture’.

Common multispectral sensors that contain RGB, red-edge, and NIR wavelengths can detect crop plant healthiness using NDVI, NDRE, or other indicis. However, these indicis cannot differentiate between certain type of stresses. Specific wavelengths in hyperspectral sensors at specific times might be more sensitive in plants under a specific type of stress than other stresses and in soil under a specific property than others. Findings by hyperspectral sensors can be applied to make sensors that can detect specific targets.

Huge data analysis from hyperspectral sensors requires robust statistical and computational methods instead of simple linear regression analysis.

So, in the Special Issue ‘Methodologies Used in Hyperspectral Remote Sensing in Agriculture’, we welcome recent experimental research or cases studies such as statistical and computational (Artificially Intelligent) methods for hyperspectral data analysis to detect specific targets which includes:

• different types of crop stress detection;
• weed type differentiation;
• crop type differentiation;
• insect/pest infestation identification;
• soil property and fertility sensing;
• using different sensors including ground, UAV, airborne, and satellite platforms.

Dr. Jiyul Chang
Guest Editor

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• hyperspectral sensor
• artificial intelligence
• machine learning
• plant healthiness
• plant stresses
• fertilizer stress
• water stress
• pest infestation
• insect infestation
• soil property
• crop type
• weed type