The problem Plentiful Harvest - Ending World Hunger solves

Every year farmers lose billions of crops in the agricultural industry due to the lack of metrics on their farming methods. As a result, thousands of acres of crops are lost each year. Our app aims to provide a solution to the environmental crisis combatting the inefficiency of food production and the environmental hazards produced by overfarmed and overcultivated plots of land that damage irrigation and sap the soil of nutrients. When given some basic information about the weather and state of the soil, our app predicts which crop will yield the most product when harvested.

Soil erosion often stems from soil mismanagement that often involves the lack of proper crop-rotation, and understanding of the type of crop needed for a specific plot of soil. Through improper soil and water management, a soil's properties may be altered so that its fertility is seriously reduced or lost for good. Excessive cultivation, for example, can wreck the structure of some soils so that they are no longer capable of holding enough moisture for growing plants. Salinization, or the accumulation of salts in the topsoil, can also have a deletrious effect on soil productivity and crop yields. In extreme cases, damage from salinization is so great that it is technically unfeasible or totally uneconomic to reverse the process. Oftentimes, when a plot of farmland is poorly managed the blowing away of top soil can result.

Our app aims to provide a solution to this problem by providing machine-learning aided computational metrics in order to suggest potential crops to the farmer based on the specific geographic layout and chemical composition of his or her farmland. By analyzing a total of 7 factors, including pH values, the concentration phosphorous, nitrogen, potassium in the soil, as well as the temperature, humidity, and rainfall unique to the region, we are able to run a predictive model to generate a recommendation for the ideal crop to farm on that specific track of land.

Challenges we ran into

The main challenge was getting our website connected to our machine learning algorithm. We decided to implement a flask localhost server to fix the issue and was able to plug in user entry fields from the front end into or machine learning algorithm to return the computed output. We next attempted to move our localhost server onto a more permanent web server on heroku. However, we encountered some difficulties fully transferring our local functionality to the cloud and will definitely continue to seek improvements in this area in the future.

Another difficulty was related to the structuring of the css and html files due to the nature of the file structures required by heroku and flask. Since both services require a specially formatted file tree with a designated 'static' folder and 'template' folder for css and html respectively, we encountered difficulty getting our website to display properly when integrated with the flask server.