FTP Students Design Device for Detecting Nitrogen Needs and Rice Plant Diseases

The front view photo of the tool design

Through the Agritech Science and Innovation Competition program organized by Agritech Research and Study Club (ARSC) Faculty of Agricultural Technology, Brawijaya University, the MVP Team developed “Integration of Machine Vision and Convolutional Neural Network on Webcam Using Raspberry Pi-3 to Identify Diseases and Nitrogen Content of Rice Plants (Oryza Sativa)”.

The team consisting of Setiyaki Aruma Nandi, Keiza Alfera Hummairo Assyura, Putri Eka Wulandari, and Zahra Cahya Ramadhani developed the device under the guidance of lecturer Dr.Agr.Sc. Ir. Dimas Firmanda Al Riza, ST., M.Sc, IPM.

A device with integrated machine vision and convolutional neural network technology on a webcam equipped with a Raspberry Pi-3 plays a role in the process of identifying diseases and nitrogen content in the rice plants.

This device was developed using the MobileNet V3 architecture with an accuracy of up to 97% so that the identification process can be carried out in real time and non-destructively. The identification process in the field is obtained through leaf image data which has been adjusted using several features to produce accurate information.

The device for identifying nitrogen content and diseases that attack rice plants has a portable design that allows farmers to carry it to various plots of agricultural land.

“Agriculture is the main sector that has an influence in supporting food security in Indonesia. However, modern technology is difficult to implement on small farms because of the high costs, so many farmers still stick with conventional methods. Therefore, we created this device with an ergonomic and economical design to make it easier for farmers to predict the nutrition and disease management needed by rice to produce superior harvest quality,” said Setiyaki Aruma Nandi, the Team Leader.

The back view photo of the tool design

This innovation is expected to provide significant changes in the agricultural sector, help farmers optimize agricultural production, and support food security in Indonesia.

 

“The detection carried out by this tool utilizes the Leaf Color Index (LCI) to determine nitrogen needs so that farmers can provide fertilizer according to recommendations. “Meanwhile, disease detection can be identified through patterns or spots on rice leaves,” continued Setiyaki.

The development of tools to detect nitrogen needs and diseases that attack rice plants will have great potential for the quantity and quality of rice plants as a staple food source in Indonesia. The predictions displayed by this tool can be used by farmers as part of planning and processing so that the use of resource can be implemented more efficiently.

“The educational facilities that support our learning activities have sparked enthusiasm for innovation and creating technology that has a positive impact on stakeholders. Through the device we have developed, it is hoped that we can realize more advanced agriculture and make a big contribution to the country,” she concluded.

Agritech Science & Innovation Competition 2023 (ASIC) is a program that facilitates students of the Faculty of Agricultural Technology in implementing the science and technology they have learned to the wider community. ASIC aims to increase students’ creativity, ideas and writing skills so that they can gain experience, recognition, discoveries, as well as relationships in developing various innovations that are beneficial to society. (UB PR/ Trans. Iir)