An IoT-Enabled Deep Learning Framework for Autonomous Environmental Monitoring and Toxicity Classification in Smart Mushroom Cultivation Systems

Monitoring and controlling the weather is an essential aspect of mushroom development, particularly the effects of temperature, humidity, light intensity and the amount of carbon dioxide. The traditional method of mushroom farming is quite challenging because there is little control over the weather and cultivation process, and poisonous mushrooms frequently grow. Hence, a sensor based self-regulating Internet of Things framework will be relatively more convenient than any conventional system for monitoring and controlling the farming environment. Mushroom farming traditionally faces challenges due to its dependence on weather conditions and the risk of cultivating poisonous varieties. To address these issues, we propose a smart mushroom farming system integrating Internet of Things (IoT) devices and Deep Learning (DL) models, including DenseNet169, ResNet50V2, and MobileNet. This system enables remote monitoring, automated cultivation, and mushroom classification. IoT components such as microcontrollers, sensors, and actuators facilitate intelligent monitoring and automation. DL algorithms classify mushrooms as edible, inedible, or poisonous, with preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Laplacian Filter enhancing classification accuracy. Using DenseNet169, our model achieves a maximum test accuracy of 95.21%.