Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey
arXiv:2006.11391 · doi:10.34048/ACC.2020.1.F1
Abstract
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping.
arXiv commentFeatured as an article at Journal of Advanced Computing and Communications, April 2020. arXiv admin note: text overlap with arXiv:1805.00881 by other authors
References in corpus (15)
- Generative Adversarial Networks
- Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges
- A Survey of Deep Learning-based Object Detection
- Active Learning for Convolutional Neural Networks: A Core-Set Approach
- Cost-Effective Active Learning for Deep Image Classification
- PlantDoc: A Dataset for Visual Plant Disease Detection
- Deep Bayesian Active Learning with Image Data
- Vine disease detection in UAV multispectral images with deep learning segmentation approach
- Overcoming Small Minirhizotron Datasets Using Transfer Learning
- Deep Convolutional Neural Network for Plant Seedlings Classification
- Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
- Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress
- Counting and Segmenting Sorghum Heads
- An Adaptive Supervision Framework for Active Learning in Object Detection
- Recognition of Pyralidae Insects Using Intelligent Monitoring Autonomous Robot Vehicle in Natural Farm Scene