An intelligent behavior-based fish feeding system

Author(s):  
Hamzah S. AlZubi ◽  
Waleed Al-Nuaimy ◽  
Jonathan Buckley ◽  
Iain Young
2021 ◽  
Vol 1072 (1) ◽  
pp. 012073
Author(s):  
P D Karningsih ◽  
R Kusumawardani ◽  
N Syahroni ◽  
Y Mulyadi ◽  
M S B M Saad

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 91948-91960
Author(s):  
Mutiu A. Adegboye ◽  
Abiodun M. Aibinu ◽  
Jonathan G. Kolo ◽  
Ibrahim Aliyu ◽  
Taliha A. Folorunso ◽  
...  

Author(s):  
Muhammad Farid Shaari ◽  
Mohammad Ezri Indra Zulkefly ◽  
Md Saidin Wahab ◽  
Faizal Esa
Keyword(s):  

2021 ◽  
Vol 5 (4) ◽  
pp. 729-738
Author(s):  
Maria Rosaria Oktaviani ◽  
Rizky Pradana

Abstract During the pandemic of covid-19, betta fish cultivation is one of the income alternatives. It makes the cultivation of betta fish is increasing. However, not all cultivators can successfully cultivate betta fish. Water quality and fish feed must be maintained in order for betta fish can grow perfectly and attractive. The problems that often encountered to the betta fish cultivator is about feeding and monitoring pH levels, which is still manual. This is can failure in betta fish cultivation of the cultivators are not disciplined. To minimize it, then will be made “Prototype Fish Feeding System and PH Monitoring Based Android”. This research is used PLC method: (a) Requirements Analysis, (b) Design, and (c) Implementation. Divided into 2 functions: Automatic Betta Fish Feeding System Based Android Function using Node MCU ESP8266 and Monitoring PH Levels Function using Arduino Uno. The result from this research is the user can control the open and close feed on the servo through an android application, the system can provide a distance of the feed supply to a user in real-time by notifications, the LCD always display pH value, the buzzer provides a sound when the water quality has reached the acid or alkaline index.  


1997 ◽  
Vol 3 (4) ◽  
pp. 289-306 ◽  
Author(s):  
Tony J. Prescott ◽  
Carl Ibbotson

The study of trace fossils, the fossilized remains of animal behavior, reveals interesting parallels with recent research in behavior-based robotics. This article reports robot simulations of the meandering foraging trails left by early invertebrates that demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the suggestion that the capacity for intelligent behavior shown by many behavior-based robots is similar to that of animals of the late Precambrian and early Cambrian periods approximately 530 to 565 million years ago.


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