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2019 |
Scheer, Claudio; Guder, Larissa Deep Learning in Agriculture: A Systematic Literature Review Undergraduate Thesis Forthcoming Undergraduate Thesis, Forthcoming. Abstract | BibTeX | Tags: Agriculture, Deep learning, Literature review @misc{larcc:claudio_larissa:TCC:19, title = {Deep Learning in Agriculture: A Systematic Literature Review}, author = {Claudio Scheer and Larissa Guder}, year = {2019}, date = {2019-06-01}, address = {Três de Maio, RS, Brazil}, school = {Sociedade Educacional Três de Maio (SETREM)}, abstract = {With the growth of computational power, the deep learning algorithms have achieved remarkable results in several areas. Agriculture is one of the areas that are using these algorithms for the most varied domains. Therefore, this work presents a systematic literature review to consolidate the state-of-the-art about the use of deep learning applied to agricultural challenges. Papers published between January 2012 and April 2019 were considered. From the 819 papers found, 230 papers were classified. We evaluated the deep learning techniques used, crops covered, data sets used, deep learning and agriculture challenges, and among other important insights. The results have shown that deep learning is successfully used for several crops in agriculture. In the livestock branch, for example, most of the works achieved an accuracy above 95%. In total, 47.2% of the papers achieved an accuracy above 95%. Consequently, there is a lot of work to be done in the area of deep learning for agriculture. Our analysis are very important to support new research that seeks to apply deep learning in agriculture and highlight the research gaps.}, howpublished = {Undergraduate Thesis}, keywords = {Agriculture, Deep learning, Literature review}, pubstate = {forthcoming}, tppubtype = {misc} } With the growth of computational power, the deep learning algorithms have achieved remarkable results in several areas. Agriculture is one of the areas that are using these algorithms for the most varied domains. Therefore, this work presents a systematic literature review to consolidate the state-of-the-art about the use of deep learning applied to agricultural challenges. Papers published between January 2012 and April 2019 were considered. From the 819 papers found, 230 papers were classified. We evaluated the deep learning techniques used, crops covered, data sets used, deep learning and agriculture challenges, and among other important insights. The results have shown that deep learning is successfully used for several crops in agriculture. In the livestock branch, for example, most of the works achieved an accuracy above 95%. In total, 47.2% of the papers achieved an accuracy above 95%. Consequently, there is a lot of work to be done in the area of deep learning for agriculture. Our analysis are very important to support new research that seeks to apply deep learning in agriculture and highlight the research gaps. |