Traditional methods of orchid disease detection rely on manual inspection, which is both labor-intensive and inefficient. Modern smart solutions integrate Internet of Things (IoT) and Artificial Intelligence (AI) technologies to address these limitations in commercial greenhouses. Our approach leverages IoT for real-time monitoring of critical environmental factors, such as temperature and humidity, to enhance disease prediction accuracy, with a specific focus on Phalaenopsis orchids. We propose OrchidTalk-v2, a novel and dynamically adaptable system designed to improve disease risk monitoring. OrchidTalk-v2 integrates an intelligent spore germination sensor with Continuous Wavelet Transform (CWT) for feature extraction, feeding the data into a three-dimensional Convolution LSTM network model. The system achieves a precision rate exceeding 93% and provides early alerts for disease outbreaks with a recall rate above 92.75%. This marks a significant advancement in prediction accuracy for orchid disease detection within controlled greenhouse environments.