Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to enhance crop production. A stable, smart aquaponic system requires estimating the fish size in real-time.
Though deep learning has shown promise in the context of smart aquaponics, most smart systems are extremely slow and costly and cannot be deployed on a large scale. Therefore, we design and present a novel neuromorphic computer that uses spiking neural networks (SNNs) for estimating not only the length but also the weight of the fish. To train the SNN, we present a novel hybrid scheme in which some of the neural layers are trained using direct SNN backpropagation while others are trained using standard backpropagation.
By doing this, a blend of high hardware efficiency and accuracy can be achieved. The proposed computer SpikoPoniC can classify more than 84 million fish samples in a second, achieving a speedup of at least 3369× over traditional general-purpose computers. The SpikoPoniC consumes less than 1100 slice registers on Virtex 6 and is much cheaper than most SNN-based hardware systems. To the best of our knowledge, this is the first SNN-based neuromorphic system that performs smart real-time aquaponic monitoring.
Siddique, Ali & Sun, Jingqi & Hou, Kung & Vai, Mang & Pun, Sio & Iqbal, Muhammad. (2023). SpikoPoniC: A Low-Cost Spiking Neuromorphic Computer for Smart Aquaponics. Agriculture. 13. 2057. 10.3390/agriculture13112057.