Deep Convolutional Neural Network for Plant Seedlings Classification
This addresses the need for efficient selective weeding in farming to increase outputs with minimal costs, but it is incremental as it applies an existing deep learning method to a new agricultural dataset.
The paper tackled the problem of reliable weed detection in agriculture by comparing traditional algorithms and a Convolutional Neural Network (CNN) for classifying plant seedlings, finding that CNN-driven applications can optimize crop yield and improve productivity.
Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies. With increasing demand for food and cash crops, 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. Previous machine vision technologies developed for selective weeding have faced the challenge of reliable and accurate weed detection. We present approaches for plant seedlings classification with a dataset that contains 4,275 images of approximately 960 unique plants belonging to 12 species at several growth stages. We compare 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 show that CNN-driven seedling classification applications when used in farming automation has the potential to optimize crop yield and improve productivity and efficiency when designed appropriately.