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The 21st century became the beginning of the development of information technology, where one of the revolutions was the presence of the Internet of Things. Internet of Things or abbreviated as IoT is a technology that combines electronic devices, sensors, and the internet to manage data and applications. The Internet of Things can be adopted in agriculture for crop management as a media for monitoring and controlling, especially in greenhouses and is called Precision Farming. The application of precision farming will be more effective in a greenhouse because it is easier to engineer similar environmental conditions. IoT development in greenhouses is using Arduino Microcontroller or Raspberry Pi Microcomputer. These devices are used because the price is low and easy to get on the market and can be designed so that technicians who have limited information technology knowledge can run it. To be able to manage greenhouses with IoT requires sensors as five senses that can detect changes that occur in the greenhouse. By using sensors, the hardware can detect what is happening in the greenhouse and make decisions based on the data acquired. Some sensors that are often used in Precision Farming are temperature and humidity sensors, soil moisture sensors, and light sensors. In the Internet of Things, the data that has been acquired by the hardware will then be transmitted wirelessly. The wireless connections used are Bluetooth, ZigBee Protocol, and Wi-Fi, where Bluetooth and Zigbee connections have a short distance between 10-100 meters, while Wi-Fi has a longer distance especially when connected to the Internet. The purpose of this paper is to understand the advantages and challenges of adopting IoT-based Precision Farming for monitoring and automation.

Determination of combining abilities and heterotic groupings of parental lines is very important in selecting the mutant lines and to decide breeding strategies for maize hybrid production. Fourty six new mutant lines used in maize breeding programs in Indonesia, were crossed to three tester lines in a lines×testers mating design. The 138 F1 hybrids, the 46 parental lines and 3 tester lines were evaluated at Arjasari, West Java, Indonesia for the following objectives: (i) to analyze line×tester data for maturity and grain yield using GGE for identifying genetic inter-relationships among parents and (ii) to identify the best combinations for maturity and grain yield in maize hybrids. The GGE biplot graphic allowed a rapid and effective overview of General Combining Ability (GCA) and Specific Combining Ability (SCA) effects of the inbred lines, best lines and tester, as well as their performance in crosses. High GCA effect for maturity and grain yield was determined based on Average Tester Coordination function of GGE Biplot. Thus, it was revealed that DR 4 is the best tester for early maturity and DR 6 for grain yield. The maximum best-parent heterosis values and the highest SCA effects resulted from mutants (10, 31, 32, 34, 36, 48) and mutants (5, 7, 10, 32, 34, 36, 37 and 48) crosses to testers (4, 6 and 8) for early maturity and grain yield, respectively. This is potentially useful in maize breeding programs to obtain high-yielding hybrids in areas with the same climate of Indonesia.

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