Low-Cost Sensing and Classification for Early Stress and Disease Detection in Avocado Plants

TL;DR

This study evaluates low-cost sensors and machine learning techniques for early detection of stress and diseases in avocado plants, demonstrating effective classification and practical deployment in real-world agricultural settings.

Contribution

It introduces a hierarchical classifier leveraging domain knowledge that improves accuracy over conventional methods and validates the system's feasibility on embedded devices.

Findings

Leaf spectral measurements are reliable for stress detection in the field.

Hierarchical classifier achieves 75-86% accuracy across genotypes.

Embedded implementation maintains accuracy with reasonable efficiency.

Abstract

With rising demands for efficient disease and salinity management in agriculture, early detection of plant stressors is crucial, particularly for high-value crops like avocados. This paper presents a comprehensive evaluation of low-cost sensors deployed in the field for early stress and disease detection in avocado plants. Our monitoring system was deployed across 72 plants divided into four treatment categories within a greenhouse environment, with data collected over six months. While leaf temperature and conductivity measurements, widely used metrics for controlled settings, were found unreliable in field conditions due to environmental interference and positioning challenges, leaf spectral measurements produced statistically significant results when combined with our machine learning approach. For soil data analysis, we developed a two-level hierarchical classifier that leverages domain knowledge about treatment characteristics, achieving 75-86\% accuracy across different avocado genotypes and outperforming conventional machine learning approaches by over 20\%. In addition, performance evaluation on an embedded edge device demonstrated the viability of our approach for resource-constrained environments, with reasonable computational efficiency while maintaining high classification accuracy. Our work bridges the gap between theoretical potential and practical application of low-cost sensors in agriculture and offers insights for developing affordable, scalable monitoring systems.