From Gas Sensors to Artificial Neural Networks: A New Precision Farming Approach for Crop Coefficient Determination - Francesco Tralli; Barbara Fabbri; Matteo Valt; Alessandro Drago; Vincenzo Guidi (Proceedings)
This study aims to utilize gaseous emissions from cultivation to predict the crop coefficient, an indirect measure of plant water stress. An array of chemiresistive gas sensors was used to collect the data, which were then processed by a simple artificial neural network algorithm. The results highlighted that the gas emissions from tomato crops can be used as a reliable indicator for determining the crop coefficient, allowing for more efficient and effective irrigation management. This research offers a potential solution to the growing issue of water scarcity in agriculture by providing a cost-effective and practical method for monitoring crop water status.