A Method for Automated Crop Health Monitoring in Large Areas Using Multi-Spectral Images and Deep Convolutional Neural Networks

Abstract

Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from O(n2) to O(log(n)) where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m2 polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions.

Plant phenotyping relevance

UAVマルチスペクトル画像、セマンティックセグメンテーション、NDVIを統合した植物レベルの健康状態評価手法が研究の中心であり、手法の構築と検証も行っている。

Code and data availability

The supplied blocks describe a UAV multispectral dataset (270 images), manual LabelMe labels, and a U-Net segmentation pipeline, but contain no data availability statement, code deposit, repository URL, or trained model release. No paper-specific public asset is identified.

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