Analytics Vidhya Janata Hack conducted on 25th & 26th July 2020
https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-in-agriculture/
Public Leaderboard Rank: 8
Private Leaderboard Rank: 6
Recently we have observed the emerging concept of smart farming that makes agriculture more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is Machine Learning — the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
Machine learning is everywhere throughout the whole growing and harvesting cycle. It begins with a seed being planted in the soil — from the soil preparation, seeds breeding and water feed measurement — and it ends when neural networks pick up the harvest determining the ripeness with the help of computer vision.
The Toxic Pesticides Though, many of us don't appreciate much, but a farmer's job is real test of endurance and determination. Once the seeds are sown, he works days and nights to make sure that he cultivates a good harvest at the end of season. A good harvest is ensured by several factors such as availability of water, soil fertility, protecting crops from rodents, timely use of pesticides & other useful chemicals and nature. While a lot of these factors are difficult to control for, the amount and frequency of pesticides is something the farmer can control.
Pesticides are also special, because while they protect the crop with the right dosage. But, if you add more than required, they may spoil the entire harvest. A high level of pesticide can deem the crop dead / unsuitable for consumption among many outcomes. This data is based on crops harvested by various farmers at the end of harvest season. To simplify the problem, you can assume that all other factors like variations in farming techniques have been controlled for.
You need to daetermine the outcome of the harvest season, i.e. whether the crop would be healthy (alive), damaged by pesticides or damaged by other reasons.
Variable Definition ID UniqueID Estimated_Insects_Count Estimated insects count per square meter Crop_Type Category of Crop(0,1) Soil_Type Category of Soil (0,1) Pesticide_Use_Category Type of pesticides uses (1- Never, 2-Previously Used, 3-Currently Using) Number_Doses_Week Number of doses per week Number_Weeks_Used Number of weeks used Number_Weeks_Quit Number of weeks quit Season Season Category (1,2,3) Crop_Damage Crop Damage Category (0=alive, 1=Damage due to other causes, 2=Damage due to Pesticides)