Biological and Agricultural

Engineering

My Work

Computer Science

our Farms

OUR NATURE

case study
a)
RESEARCH

Biological Engineer Agricultural Engineer work to improve the quality and increase the production of farm products.

B)
Machine Learning

Machine Learning techniques are used in crop management processes, following with farming conditions management and livestock management. ... In they are used to predict yield and quality of crops as well as livestock production.

C)
OPTIMIZE

It is multi variant and highly interlinked in nature. Inputs to agriculture are many and few of them are dwindling very fast. To make agriculture sustainable and profitable as well, it is necessary to allocate the resources judiciously.

The beauty of Biological & Agricultural Engineering is that it is a culture of farming.

I aM,

Raul Sebastian Martinez
B.S. Biological & Agricultural Engineering
Minor in Computer Science
Texas A&M University College Station – Expected May 2021
B.S. in Biological and Agricultural Engineering – Minor in Computer Science

Northwest Vista College –  December 2017
San Antonio, Texas
Associates of Science
Currently a senior at the department of Biological and Agricultural Engineering (BAEN) at Texas A&M University at College Station.

Areas of study
From my BAEN courses, I have learned about the information required to sustain and manage crops in a suitable manner to achieve maximum productivity. However, currently there is an intriguing situation in the agricultural sector in terms of lack of publicly available data, lack of standards in data collection, and lack of data sharing. Therefore, I study and continuously try to expand my knowledge on the application of modern information and communication technologies into agriculture.  

Areas of Interest
Now, as the Fourth Agricultural Revolution progresses, on-farm data collection in conjunction with machine learning (ML) methods actively demonstrate the potential they have to automate and significantly improve the accuracy, computational efficiency, and cost of various tasks in precision agriculture. Machine learning-based systems are promising in agriculture because they can process a large number of inputs and handle nonlinear tasks.

Programming languages: Proficient in C++, MATLAB. Familiar with Python, Java.

Spoken languages: Advanced English and Spanish.
TESTIMONIALS

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DR. Juanito , Profesor of Texas A&M

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DR. Juanito , Researcherof Texas A&M

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Robert Shapiro, CEO of trulr

Vestibulum sed commodo nunc, eu aliquet sem. Curabitur semper, sem ut posuere tincidunt, sem velit sollicitudin odio, quis hendrerit sapien nunc sit amet sem. Ut sollicitudin dignissim ligula nec porta. 

Robert Shapiro, CEO of trulr