Multifaceted Crop Health scouting based on Farmbot and Machine Vision

Abstract

In the field of precision agriculture, integration of multi-modal data sources such as soil Nitrogen- Phosphorus-Potassium (NPK) sensors with high-resolution RGB and infrared imagery is essential for improving crop monitoring. Aligning these multi-modal data, which vary widely in resolution and format, to a uniform spatial resolution is a challenge. This research proposes a novel approach to integrate data collected from multiple sources with varying spatial resolutions and unify them to the same spatial resolution. Point-wise data is collected using NPK sensors to map the basic macronutrients (Nitrogen, Phosphorus, and Potassium) using state-of-the-art technology known as FarmBot. After data collection, heat maps are generated to represent nutrient distribution as the initial layer. Image up-sampling is performed using a deep learning algorithm called Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) on these low-resolution heat maps to enhance their resolution without losing critical features. Additionally high-resolution drone images are collected, encompassing Red-edge and Near-Infrared (NIR) bands in addition to RGB bands, for a broader health analysis of the crop. Multi-source images are integrated into a single multichannel image that includes both soil nutrient data and multispectral information. Hyperspectral imaging is used to measure soil water content through reflectance, providing additional depth to the reflectance-based soil analysis. By deploying these methods, farmers and agronomists are equipped to make more informed decisions, leading to better agricultural outcomes.