In the heart of Malaysia, researchers are tackling one of the world’s most pressing issues: water scarcity. Baraa H. Jawad, a researcher at the School of Computer Science, Universiti Sains Malaysia, has developed a groundbreaking approach to reduce water wastage in agricultural irrigation. His work, published in a journal called ‘IEEE Access’ (which translates to ‘IEEE Open Access’), could revolutionize the way we think about water conservation in agriculture.
Jawad’s research focuses on precision farming and smart irrigation, two critical areas that can significantly impact water usage in agriculture. “The global demand for food and water is increasing rapidly,” Jawad explains. “Many countries are consuming excessive amounts of scarce freshwater resources, and a large portion of this water is used for irrigation.”
To address this issue, Jawad proposes a hybrid activation function based on the Artificial Neural Network (ANN) algorithm. This function, known as TanElu, combines the Tanh and ELU functions to classify the need for irrigation in various crops and predict the best time of the day for watering. The results are impressive: the model achieved an accuracy of 98.24% in irrigation classification and 97.31% in predicting the best irrigation time. Moreover, the execution time for these tasks was remarkably efficient, at 7.88 and 8.28 seconds respectively.
The implications of this research are far-reaching. For the energy sector, which is closely linked to water usage, this technology could lead to significant savings. “The proposed model can significantly reduce water loss during the irrigation process,” Jawad states. “This represents an encouraging step toward sustainable agriculture and water conservation.”
But how might this research shape future developments in the field? One possibility is the integration of this technology into existing irrigation systems. Farmers could use this model to optimize their water usage, leading to more efficient and sustainable farming practices. Additionally, this research could pave the way for further advancements in precision farming and smart irrigation, potentially leading to even more accurate and efficient water management systems.
Moreover, the success of the TanElu function suggests that hybrid activation functions could be a promising area of research in the field of artificial neural networks. This could lead to advancements in other areas that rely on ANN, such as image recognition, natural language processing, and predictive analytics.
In the face of a growing global population and increasing demand for food and water, innovations like Jawad’s are more important than ever. As we strive for a more sustainable future, technologies that can help us conserve our precious resources will be invaluable. And with researchers like Jawad leading the way, the future of water conservation in agriculture looks bright.