Plant Seedling Classification

Agriculture is necessary for human continuity and remains a major driver of several economies around the world; more so in underdeveloped and developing economies. As demand for food and cash crops is increasing, due to a growing global population and the challenges posed by climate change, there is a pressing need to increase farm outputs while incurring minimal costs. Some previous technologies developed for selective weeding have faced the challenge of reliable and accurate weed detection. We are presenting approaches for plant seedlings classification with a dataset which contains total 4000 plus images of approximately 900 plus unique plants belonging to 12 category at different increasing stages. We compared the performances of two traditional algorithms and a Convolutional Neural Network (CNN), a deep learning technique widely applied to image recognition, for this task. Our findings shows that the CNN-driven seedling classification applications when used in farming automation has optimized the crop yield and help in improving productivity.