Deep Convolutional Neural Network for Plant Seedlings Classification
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
A Public Image Database for Benchmark of Plant Seedling Classification Algorithms
A database of images of approximately 960 unique plants belonging to 12 species at several growth stages is made publicly available. It comprises annotated RGB images with a physical resolution of roughly 10 pixels per m…
General ClassificationEstablish seedling quality classification standard for Chrysanthemum efficiently with help of deep clustering algorithm
Establishing reasonable standards for edible chrysanthemum seedlings helps promote seedling development, thereby improving plant quality. However, current grading methods have the several issues. The limitation that only…
ClusteringDeep ClusteringUsing a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals
Identification of salt tolerance of crops usually requires long-term observation of morphology, or physiological and biochemical experiments, which are time-consuming and laborious tasks. This paper proposes a model, b…
Data AugmentationGenerative Adversarial NetworkStimulation of soy seeds using environmentally friendly magnetic and electric fields
The study analyzes the impact of constant and alternating magnetic fields and alternating electric fields on various growth parameters of soy plants: the germination energy and capacity, plants emergence and number, the …
Mutation activity of Lonicera caerulea population in an active fault zone (the Altai Mountains)
Geophysical and geochemical anomalies may have a mutagenic effect on plants growing in active fault zones being the factors of evolutionary transformation of plant populations. To test this assumption we evaluated the mu…