To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, face challenges in controlling critical physico-chemical parameters such as temperature, humidity, and gaseous emissions. Additionally, these methods require significant labor and over 100 days to achieve compost maturity. To address these issues, we propose an intelligent, automated composting system leveraging the Internet of Things (IoT) and wireless sensor networks (WSNs). This system integrates sensors for real-time monitoring of key parameters: DS18b20 for waste temperature, HD-38 for humidity, DHT11 for ambient conditions, and MQ sensors for detecting CO₂, NH₃, and CH₄. Controlled by an ESP32 microcontroller unit (MCU), the system employs a mixer and heating elements to optimize waste degradation based on sensor feedback. Data transmission is managed using the MQTT protocol, allowing real-time monitoring via a cloud-based platform (ThingSpeak). Furthermore, the degradation process was analyzed during the first 24 h, and a recurrent neural network (RNN) algorithm was employed to predict the time required for reaching optimal compost maturity, ensuring an efficient and sustainable solution.

Salinity problems reduce productivity on both irrigated and non-irrigated agricultural lands. Saline soils occur in arid and semi-arid regions where rainfall is insufficient to meet the crops’ water requirements and leach mineral salts out of the root-zone. Compost can play an important role in managing, mitigating, and improving the soil salinity negative effects. In this work, the compost effects on salt affected soil were studied. Therefore, physico-chemical parameters and morphological status of the plants divided into four plots in a greenhouse were studied. The first plot P0 is a control plot which was irrigated by fresh water, P1 was irrigated with fresh water mixed with compost, P2 and P3 were irrigated by saline water with an NaCl concentration of 5 g/L. An automated irrigation system was implemented to manage plots irrigation. The system was based on an electronic board and multiple sensors to track ambient temperature and humidity in the greenhouse, soil moisture and temperature of each plot, data was stored in a SD card. The results obtained showed that compost use increased stem growth by 23%, this percentage was improved by 10% for greenhouse tests.