We investigated the long term functionality of the designed PMCS in a practical use case where we monitored plant growth of classical horticulture Dianthus flowers (Dianthus carthusianorum) under the effect of plastic mulching over a period of 4 months. This use case represents a common phenological monitoring case that can be used in agricultural studies. For this, we integrated our PMCS in an embedded vision camera equipped with the openMV H7 Plus board in a waterproof housing [26]. The system shown in Fig.

These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings.  Odors of different 12 samples are taken from these six sensors

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Black Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 

9- Orange

10- Colonia Perfume

The paucity of data has long hindered the accurate modeling of CO2 concentrations within peatland regions, despite their significance as carbon reservoirs. Peatlands naturally sequester substantial carbon underground, yet disturbances, whether due to climate change or land use shifts, can trigger the release of significant amounts of carbon and other greenhouse gases, thereby disrupting the atmosphere and impacting human lives. The lack of comprehensive data has rendered it challenging to thoroughly assess the peatland regions' contribution to the net ecosystem carbon budget (NECB).

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.