tigasgon1999/Agricultural-Pattern-Recognition
This project focuses on using the Semantic Segmentation Deep Learning architecture DeepLAbV3+ on the Agriculture-Vision dataset. We focus on improving the architecture's performance by solving the class imbalance problem present in the data.
This project helps agricultural specialists accurately identify various patterns in farmland, such as weed clusters, waterways, or planted rows, from aerial imagery. It takes in satellite or drone images of agricultural land and outputs precise maps highlighting these different features. Farmers, agronomists, or land management professionals can use this to get a better understanding of their fields.
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Use this if you need to precisely identify and map specific agricultural features across large areas of farmland using aerial images, even when some features are rare or hard to detect.
Not ideal if you are looking for a simple, off-the-shelf solution for general object detection or if you don't work with high-resolution aerial imagery.
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Last pushed
Nov 26, 2022
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