Mohamed Habib Sellami; Farhat Sahbani
Abstract
Farmers and experts are continuously searching for optimal conditions to improve the productivity of tomatoes in greenhouses. To provide an answer to their concerns, we have developed a modeling approach to automate the functioning of a greenhouse cultivated with tomato plants. For the aeration, heating, ...
Read More
Farmers and experts are continuously searching for optimal conditions to improve the productivity of tomatoes in greenhouses. To provide an answer to their concerns, we have developed a modeling approach to automate the functioning of a greenhouse cultivated with tomato plants. For the aeration, heating, and irrigation systems, we compared the Proportional Integral Derivative (PID) controller response to the Fuzzy logic (FL) controller response. For the aeration system, the response of both controllers was stable, with a pick of about 1.09 for the PID controller and zero for the fuzzy controller. Likewise, there was no overtaking for the fuzzy controller but about 8.28% for the PID controller. The rise time for the fuzzy controller was less than that of the PID controller (627 s). We signaled a stable response for the PID controller and the fuzzy logic controller for the irrigation system. The pick and the overtake were equal to zero for the fuzzy logic controller but were 1.28 and 28.2 s for the PID controller, respectively. In the case of both controllers, the rise time was the same, equaling 18.3 s. The regulation time was less than 35 s for the fuzzy logic controller and 31.1 s for the PID controller.
Mandana Mahfeli; Saeid Minaei; Ali Fadavi; Shirin Dianati
Abstract
The synthetic seed method refers to encapsulated plant parts and any meristematic tissue which can develop into plantlets under in-vitro or in-vivo conditions. various parameters and evaluating’ one-variable-at-a-time’ could be time-consuming, expensive, and inefficient. Thus, the application ...
Read More
The synthetic seed method refers to encapsulated plant parts and any meristematic tissue which can develop into plantlets under in-vitro or in-vivo conditions. various parameters and evaluating’ one-variable-at-a-time’ could be time-consuming, expensive, and inefficient. Thus, the application of process modeling approaches including Multi-Layer Perceptron (MLP) and the Radial-Basis Function (RBF) can be required and beneficial for the prediction of synthetic seed weight. In the present study, two different types of artificial neural network (ANN) algorithms, the MLP and RBF models, have been developed to predict the weight of Phalanopsis orchid synthetic seed using an encapsulation set-up especially developed for this purpose. Various topologies of ANN were configured based on different concentrations of sodium alginate (3, 4, and 5 (w/v)), calcium chloride (100,125, and 150 (mM), and droplet falling height of sodium alginate (1, 1.5, and 2 cm) as input variables and the values of synthetic seed weights as output variable. Results show that the RBF algorithm (R= 0.98 and SSE= 0. 13× 10-3) outperformed the MLP algorithm (R = 0.91and SSE= 0.14× 10-3) owing to its better ability for predicting capsule weight. The study has presented a machine learning-based approach for the classification of synthetic seeds. Algorithms for extraction of capsule features have been developed, which are in turn used to train artificial neural network (ANN) classifiers. The outputs of ANNs have been successfully applied to model the synthetic seeds production process indicating the appropriateness of the model equation in predicting orchid synthetic seed weight are mathematically combined.
