Deep learning based computer vision approaches for smart agricultural applications

作     者：V.G.Dhanya A.Subeesh N.L.Kushwaha Dinesh Kumar Vishwakarma T.Nagesh Kumar G.Ritika A.N.Singh 

作者机构：ICAR-Indian Institute of Seed ScienceMauUttar Pradesh 275101India ICAR-Central Institute of Agricultural EngineeringBhopalMadhya Pradesh 462038India ICAR-Indian Agricultural Research InstituteNew Delhi 110012India Govind Ballabh Pant University of Agriculture and TechnologyPantnagarUttarakhand 263145India ICAR-National Institute of Natural Fibre Engineering and TechnologyKolkata 700040India 

出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)

年 卷 期：2022年第6卷第1期

页      面：211-229页

核心收录：

学科分类：0710[理学-生物学] 120301[管理学-农业经济管理] 12[管理学] 1203[管理学-农林经济管理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：Agriculture automation Computer vision Deep learning Machine learning Smart agriculture Vision transformers 

摘      要：The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied *** the core of artificial intelligence,deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into *** vision techniques,in conjunction with high-quality image acquisition using remote cameras,enable non-contact and efficient technology-driven solutions in *** review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to *** works in the area of computer vision were analyzed in this paper and categorized into(a)seed quality analysis,(b)soil analysis,(c)irrigation water management,(d)plant health analysis,(e)weed management(f)livestock management and(g)yield *** paper also discusses recent trends in computer vision such as generative adversarial networks(GAN),vision transformers(ViT)and other popular deep learning ***,this study pinpoints the challenges in implementing the solutions in the farmer’s field in *** overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and ***,the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.