Low-Cost Imaging Systems for Early Identification of Crop Stress

Rohan Malhotra

Low-Cost Imaging Systems for Early Identification of Crop Stress

Author(s)	Rohan Malhotra
Country	United States
Abstract	Early identification of crop stress is essential for preventing avoidable yield losses, improving input-use efficiency, and supporting timely crop-management decisions. Conventional field scouting remains important but often identifies stress only after visible symptoms have developed or requires substantial labor, technical expertise, and repeated field observation. Advanced hyperspectral and high-resolution thermal systems can reveal subtle physiological changes but their acquisition cost, calibration requirements, data-processing burden, and operational complexity can restrict adoption in resource-constrained agricultural settings. This study examines the potential of low-cost imaging systems based on smartphone red-green-blue imaging, compact thermal sensors, and inexpensive multispectral modules for early crop-stress identification. Because no original experimental dataset was supplied, the study adopts a transparent simulation-based comparative design. Four hypothetical imaging configurations were evaluated: smartphone RGB imaging, low-cost thermal imaging, RGB–thermal fusion, and RGB combined with low-cost multispectral sensing. A synthetic evaluation framework representing 400 crop-observation scenarios was developed across water stress, nutrient deficiency, heat stress, and early disease-related visual disturbance. Five assessment dimensions were considered: early-detection capability, stress-discrimination potential, field usability, computational affordability, and environmental robustness. The simulated composite early-stress identification scores were 68 for smartphone RGB, 76 for low-cost thermal imaging, 84 for RGB–thermal integration, and 88 for RGB–multispectral integration. The analysis indicates that the principal advantage of low-cost systems does not arise from replacing laboratory-grade sensors with cheaper hardware alone. Their practical value depends on calibration, standardized image acquisition, environmental compensation, appropriate machine-learning models, and the integration of complementary sensing modalities. Recent research demonstrates that low-cost thermal and multispectral devices can support plant-stress monitoring, while smartphone-based systems are increasingly feasible for field deployment. However, sensor variability, changing illumination, crop-specific spectral responses, and model generalization remain critical limitations. The paper proposes a Low-Cost Crop Stress Imaging Framework integrating accessible hardware, image standardization, lightweight analytics, confidence-based alerts, and farmer-oriented decision support. The proposed framework provides a methodological foundation for future field validation in precision agriculture, smallholder farming, protected cultivation, and extension-based crop-health monitoring.
Keywords	: crop stress detection; low-cost imaging; RGB imaging; thermal imaging; multispectral sensing; smartphone agriculture; machine learning; precision agriculture; plant phenotyping; early stress identification
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2021
Published On	2021-05-16

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American Journal of Advanced Multidisciplinary Innovation and Research

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Low-Cost Imaging Systems for Early Identification of Crop Stress

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