My interest in Computer Science began during downtime in physical therapy, when I started exploring online coding courses. Upon seeing my first image recognition correctly identify a crop with blight and continue to do so with about 96 percent accuracy, I realized code is more than syntax. It is a method to recognize patterns, build solutions, and solve real problems. Growing up around farms, I saw how unpredictable weather can determine a farmer’s financial stability. Entire harvests used to change because of a shift in rainfall or humidity levels. This made me wonder whether technology could help farmers plan. This was the first time Computer Science felt personal. As a student in iDTech, I strengthened my Python skills and learned to approach problems like a programmer by breaking large tasks into smaller modules, testing outcomes, and choosing the right tools for each step. Working through different APIs taught me how every method could be used to build something meaningful. I began developing an AI model in the summer of 2025 to help farmers with crop cycling by creating harvest schedules and reducing weather risk. I built a program that used data on weather, region, and season to help maximize profit margins. When I returned to my rural hometown, I collected information on soil quality, annual rainfall, and humidity, and applied the model to a small section of farmland. By the end of the season, the farmers reported higher crop yields and increased profits. For many summers, my family returned to India, where I began my journey of service. During COVID, when stores hiked their prices, many families in my area could not afford basic produce. One idea I had was to create a website where families that bought directly from our farm could quote a price they felt comfortable paying and would get their order delivered. I built the website and publicized it through social media, and set up flyers across the neighborhood. Many times, while I was personally doing door-to-door delivery, people shared how this service helped them with comments such as “We needed something like this in our neighborhood, thanks!” This showed me that my actions had measurable results and meaning. Seeing these results changed how I viewed Computer Science. It showed me that technology can support communities in ways that matter every day. This experience strengthened my commitment to use data and programming to assist under-resourced regions. In college, I hope to deepen my understanding of predictive modeling and develop more effective tools that help communities like the one I grew up in. I worked with low-income farmers to get their opinion on the sales of the crop. With their advice, I went to the Farmers’ Associations and discussed how prices could be altered. The system I set up helped lower costs for dozens of families during COVID and provided farmers with predictable income, and seeing it continue to function even after I moved to the States showed me that my efforts can make a difference, no matter where I am. The results have only increased my desire to practically help the underserved.