Taleus

According to FAO (Food and Agricultural Organization of the United Nations), by 2050 we’ll need to feed two billion more people. Our demands for food will increase by 70%. Meanwhile, farmers throughout the world struggle to prevent and eradicate various diseases from their crops. It’s estimated that various pests (insects, weeds, nematodes, animals, diseases) each year cause crop yield losses of 20-40%. More precisely, there is some data that maintains that crop diseases cause average yield losses of 42% for the most important food crops. In some cases, crop diseases destroy the whole crop production. This is ridiculous. We can't afford to lose any food when we need more of it. For this reason, it's extremely important for farmers to find out all they can about the crop diseases so they can manage them properly. The rapid, accurate diagnosis of disease and its management will help to reduce yield losses. But, the infrastructure for crop disease diagnostic is limited. One potential application is the development of mobile disease diagnostics through machine learning. It's widely predicted that by 2020, there will be about 5 billion smartphones distributed across farmers around the world. Offering the potential of turning the smartphone into a valuable tool for diverse communities growing food. That's why we built a chatbot to help farmers diagnose crop disease called Dr. Tania. Dr. Tania is a deep learning based chatbot that could identify crop diseases through image recognition. She can also tell the farmer about symptoms, and how to manage the disease correctly and effectively. References: [1] http://www.fao.org/news/story/en/item/280489/icode/ [2] https://aglifesciences.tamu.edu/rootbiome/wp-content/uploads/sites/38/2015/06/2016-Ficke-et-al-CropLosses-FoodSecurity-Research-gate.pdf [3] http://www.fao.org/docrep/014/am859e/am859e01.pdf [4] http://www.mdpi.com/journal/agriculture/special_issues/plant_disease [5] http://blog.agrivi.com/post/crop-diseases-the-nightmare-of-every-farmer [6] https://www.ncbi.nlm.nih.gov/pubmed/27713752

Our team, Taleus, consists of three members namely Febi Agil Ifdillah, Ahmad Farhan Ghifari, and Harry Alvin Waidan Kefas. We want to embrace data-driven farming and democratize precision farming technologies. Tania is one of several affordable solutions that we want to roll out to the market so that everyone will have access to high-quality farming technologies. We need more of these technologies to help ourselves to provide enough food for mankind in the future. Febi Agil Ifdillah is student currently pursuing Bachelor's Degree at School of Electrical Engineering and Informatics (SEEI), Bandung Institute of Technology and majoring Informatics/Computer Science. He is comfortable with several programming languages including Java, C/C++, Python, Javascript, and Scala as well as other technologies. Now, He is focusing his career towards Data Science or Machine Learning. During his college, Febi has won several competitions ranging from hackathons to Data Mining at national and regional level. In November 2017 he receives an award (along with his teammate) as the 1st winner of Data Mining Competition in Gemastik 2017, a nationwide competition held by Indonesia Ministry of Research, Technology, and Higher Education. Ahmad Farhan Ghifari is a student of Informatics/Computer Science at Institut Teknologi Bandung. He is an expert in java programming. He also likes to work in user experience designer. In Taleus, Ghifari chose to handle the front end of the created apps. Ghifari has handled much of the making of mobile apps. Beside java, he also likes to work with C# language to program and XAML to make beautiful design. He also used his free time to explore more about Azure Service. Ghifari has won three hackathon competitions. Favored research focuses on disease resolution using technology. He was also interested in doing some experiments using arduino. Arduino and mobile programming are the most preferred technologies. Harry Alvin Waidan Kefas is a student of Informatics/Computer Science Major at Institut Teknologi Bandung. His first-learned programming language is Pascal. Kefas preferred position in development team is backend development. His primary programming language is Node.js. He used to create a lot of service to serve data and collect data. To collect data, he prefer to use Python. Beside Node.js to serve data, he also like to use Python with microservice framework Flask. Kefas already participate in several hackathons. His team has been got the podium for 3 over 4. He is enthusiastic in exploring new technology or technique to solve problems met around him.