We present a systematic evaluation of low-cost soil sensors for early stress and disease detection in avocado plants. Our monitoring system was deployed across 72 plants divided into four treatment categories within a controlled greenhouse environment collecting data over six months. We developed a two-level hierarchical classifier leveraging soil electrical conductivity (EC) and moisture data to improve classification accuracy. The proposed classifier achieved 75--86% accuracy across different avocado genotypes, outperforming conventional machine learning approaches by over 20%. Our findings demonstrate that while low-cost sensors exhibit certain limitations in field conditions, strategic classification techniques can significantly enhance their utility for precision agriculture.

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.