Real time automated pest monitoring of horticultural crops

Horticultural production plays a vital role in sustaining dietary quality worldwide, yet its efficiency is threatened by insect pest and diseases infestation. Pest damage is becoming more severe as climate variability alters pest behavior and distribution, while insufficient monitoring and reliance on broad-scale, non-targeted management approaches further limit effective control. Also, conventional methods of pest surveillance are mostly labour-intensive, time-consuming and prone to human error, leading to delays in detection and suboptimal decisions on pest management. Recent breakthroughs in AI, machine learning, remote sensing, sensor networks and loT have paved the way for real-time automated monitoring systems for pests, which can provide continuous, accurate and high-resolution information about pest populations. These systems integrate with computer-vision algorithms, deep learning models, multispectral imaging, optoelectronic sensors, UAV-based surveillance and cloud-linked data platforms to detect, classify and quantify pest species under diverse field conditions. Real-time data acquisition, along with geo-referenced mapping, multi-modal data fusion and predictive modeling, narrows the gap in outbreak forecasting while strengthening decision-support systems. The advances facilitate site-specific interventions, reduce prophylactic application of pesticides and enhance the sustainability and resilience of integrated pest management programmes. Despite substantial progress, challenges persist in the form of sensor calibration, power limitations, cost barriers and model generalization across heterogeneous environments. In general, real-time automated pest monitoring is a game-changing concept for modern horticulture, with great potential to optimize pest surveillance and minimize crop losses and support climate-adaptive and technology-driven production systems.