Forecasting Post-Harvest Tomato Shelf Life with Recurrent Neural Networks and Electronic Nose Data
Lionel Affognon, Eugène C Ezin, Chérif Diallo
Informations
- Revue / Journal
- SN Computer Science
- Éditeur
- Springer Nature Singapore
Résumé
The automation of agriculture is increasingly incorporating advanced technologies like Internet of Things (IoT) and artificial intelligence to optimize productivity. This study focuses on a specific innovation: forecasting post-harvest tomato shelf life using electronic noses and deep learning. The proposed approach provides accurate and continuous monitoring of tomato conditions, addressing the industry’s demand for enhanced efficiency and quality. The model architecture includes four layers: two stacked recurrent layers (either long short-term memory (LSTM) or gated recurrent unit (GRU)), a dropout regularization layer, and a dense output layer. The LSTM based model achieved a mean squared error (MSE) of 0.000586, a mean absolute error (MAE) of 0.01502, and R-squared values of 0.817 (global) and 0.9870 (remaining days). Similarly, the GRU based model demonstrated superior performance with an MSE of …