A Web Application and Database for Agriculture Genetic Diversity and Association Studies

Self Cite

Indonesia’s crude palm oil (CPO) production from year to year continues to increase, at the end of 2020 it reached 17.35 million tons, up 3.6% from the previous year. Increasing production will result in more CPO stock and require good storage. The storage process that occurs is to maintain the temperature of the CPO so that the quality is not damaged. This temperature regulation is still done manually and raises the risk of work accidents. The purpose of this research is to create a temperature control system and automatic volume measurement that can be monitored from a smartphone. The manufacture of a control system used ESP8266 NodeMCu microcontroller, temperature sensor, proximity sensor, and 1000-Watt heater. Programming used the Arduino IDE and C++. The result of this study was an IoT CPO Storage Tank design equipped with sensors and microcontrollers. The temperature was measured with the DS18B20 sensor had a data accuracy of 99.19% while the volume measured with the HC-SR04 sensor had an accuracy of 99.78%. Data obtained from the sensor could be seen through the Thingspeak application from a computer or smartphone.

Section: Related Workmentioning

confidence: 99%

Smart IoT CPO Storage Tank

Renjani

Hermantoro

Nugraha

et al. 2022

Self Cite

“…The development of information systems via mobile or web applications aims to provide two essential features: weed and herbicide data analysis via an AI-based recognition model and data management [26]. We have built such similar systems in our several previous agricultural-related research to support the above two features [27][28][29][30].…”

Section: Prediction Process and Generating Applicationmentioning

confidence: 99%

Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

Firmansyah

Suparyanto

Hidayat

et al. 2022

Self Cite

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

“…This approach employs relatively short DNA sequences from a particular region of the genome of an organism [5]. The DNA barcoding approach has been developed significantly fast, so it becomes a powerful tool not only to investigate biodiversity, but also to monitor the evolution of organisms [6][7][8]. Therefore, we can easily find organisms with unique and superior traits with this approach.…”

Section: Introductionmentioning

confidence: 99%

Identification of rice plants via DNA barcoding for securing future food availability

Trinugroho

Sudigyo

Hidayat

et al. 2023

Given the continuous increase in the world population, producing more food is necessary. The majority of people across the world consume rice as the main food. Thus, it is essential to produce more rice plants to be able to feed the growing world population. Molecular biology techniques have advanced rapidly, allowing the exploration and discovery of various plants by examining their DNA genome, which is called DNA barcoding. This paper aims to develop a pipeline for the identification and exploration of rice plants by utilizing a DNA barcoding approach. We chose the rbcL gene as a target region for DNA barcoding. Then, we retrieved the sequences of different rice species from the online database. After performing sequence analysis, a set of primers were designed, which were then used for in-silico PCR amplification. We have shown that the designed primers have fulfilled the desired criteria. In addition, the gel electrophoresis confirmed the successful amplification of the rbcL gene using the designed primers. Hence, this study can be used to identify rice species or variants with superior traits. This may help to solve the food availability issue in the future.