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.

Honeybees, as natural crop pollinators, play a significant role in biodiversity and food production for human civilization. Bees actively regulate hive temperature (homeostasis) to maintain a colony’s proper functionality. Deviations from usual thermoregulation behavior due to external stressors (e.g., extreme environmental temperature, parasites, pesticide exposure) indicate an impending colony collapse. Anticipating such threats by forecasting hive temperature and finding changes in temperature patterns would allow beekeepers to take early preventive measures and avoid critical issues. In that case, how can we model bees’ thermoregulation behavior for an interpretable and effective hive monitoring system? In this article, we propose the principled Electronic Bee-Veterinarian Plus (EBV+) method based on the thermal diffusion equation and a novel “ sigmoid ” feedback-loop (P) controller for analyzing hive health with the following properties: (i) it is effective on multiple, real-world beehive time sequences (recorded and streaming), (ii) it is explainable with only a few parameters (e.g., hive health factor) that beekeepers can easily quantify and trust, (iii) it issues proactive alerts to beekeepers before any potential issue affecting homeostasis becomes detrimental, and (iv) it is scalable with a time complexity of \(O(t)\) for reconstructing and \(O(t\times m)\) for finding m cuts of a sequence with t time-ticks. Experimental results on multiple real-world time sequences showcase the potential and practical feasibility of EBV+. Our method yields accurate forecasting (up to 72% improvement in RMSE) with up to 600 times fewer parameters compared to baselines (ARX, seasonal ARX, Holt-winters, and DeepAR), as well as detects discontinuities and raises alerts that coincide with domain experts’ opinions. Moreover, EBV+ is scalable and fast, taking less than 1 minute on a stock laptop to reconstruct 2 months of sensor data.

Honeybees are vital for pollination and food production. Among many factors, extreme temperature (e.g., due to climate change) is particularly dangerous for bee health. Anticipating such extremities would allow beekeepers to take early preventive action. Thus, given sensor (temperature) time series data from beehives, how can we find patterns and do forecasting? Forecasting is crucial as it helps spot unexpected behavior and thus issue warnings to the beekeepers. In that case, what are the right models for forecasting? ARIMA, RNNs, or something else?

Non-biodegradable product usage and ignorance towards proper disposal are creating the problem of ever-growing trash stacks. An autonomous mobile trash collector which collects trash lying on ground in a trash-container attached to it can be a feasible solution to the problem. In this procedure trash detection is done via deep learning algorithm. An ultrasonic sonar sensor on the robot detects object along the path and a camera module sends pictures of the object to raspberry pi for classification into trash or not trash. The prototype robot is of low-cost and can detect a wide range of trash with high accuracy. Therefore has good environmental as well as economic impact.