Optimization of Climatic Conditions Affecting Determination of the Amount of Water Needed by Plants in Relation to Their Life Cycle with Particle Swarm Optimization, and Determining the Optimum Irrigation Schedule

Abstract

Plants’ need for water has become a topic of research for the agriculture industry. The fact that plant species are very diverse and each plant’s need for water varies makes it difficult to plan programs with conventional irrigation methods. Plants exhibit different stages from their seed time to harvest season. Each stage is defined within as days, and the amount of water needed by the plant throughout these stages varies. In this study, optimization of the irrigation schedule for each stage of a plant is provided. The amount of water needed by the plant was first figured out by using climatic data, and then, these values were recalculated in relation to the plant type. The amount of water needed at each stage was related to the plant type by using particle swarm optimization. The obtained results revealed the optimal irrigation schedule for each stage with the obtained data.

A Hybrid Artificial Neural Network—Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings

“…Other parameters used in PSO were determined as a result of studies, as presented in Table 7. As can be seen in Table 7, the momentum constant (w) value was taken as 1 in order to eliminate sudden velocity changes and not destroy the velocity concept in the algorithm, and C 1 and C 2 values were chosen to be C 2 + C 2 ∼ = 4 [31]. The algorithm converges as the number of iterations increases.…”

“…PSO is an optimization technique that mimics the behavior of a swarm in nature. In PSO, each individual in a population represents a part of a solution, and it is aimed to find a better solution as a result of interactions between individuals [31]:…”

“…Artificial neural networks (ANN), which is one of the machine learning methods, and particle swarm optimization (PSO), which is used in solving multidimensional problems, are frequently used in many areas [27][28][29][30][31][32]. In structural and earthquake engineering applications, these techniques have a critical impact on areas such as simulation, modeling, optimization, regression, and classification [33,34].…”

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

“…where V ij represents the velocity of particle j at iteration i; rand 1 and rand 2 represent random values generated in the range [0-1]; c 1 represents the cognitive learning ability within the swarm, while c 2 represents the social learning ability within the swarm; X ij represents the position of particle j in iteration i; pbest i represents the best particle at iteration i; and gbest i represents the global best particle [16].…”

“…The hyperparameters in Table 4 directly affect the success performance of the network. In such a problem, with a nine-dimensional solution space, the most successful architecture and hyperparameter values can only be determined by optimization algorithms [16,38].…”