RNN- and CNN-based weed detection for crop improvement: An overview

Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant c...

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Autores principales:	Brahim Jabir, Loubna Rabhi, Noureddine Falih
Formato:	article
Lenguaje:	EN
Publicado:	Kemerovo State University 2021
Materias:	digital agriculture weed detection machine learning deep learning convolutional neural network (cnn) recurrent neural network (rnn) Food processing and manufacture TP368-456
Acceso en línea:	https://doaj.org/article/76c47661b439482785715b42fe919e86
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spelling	oai:doaj.org-article:76c47661b439482785715b42fe919e862021-11-19T04:02:56ZRNN- and CNN-based weed detection for crop improvement: An overview2308-40572310-959910.21603/2308-4057-2021-2-387-396https://doaj.org/article/76c47661b439482785715b42fe919e862021-11-01T00:00:00Zhttp://jfrm.ru/en/issues/1879/1961/https://doaj.org/toc/2308-4057https://doaj.org/toc/2310-9599Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant classification, fruit counting, pest identification, and weed detection. The latter was the subject of our work. Weeds are harmful plants that grow in crops, competing for things like sunlight and water and causing crop yield losses. Traditional data processing techniques have several limitations and consume a lot of time. Therefore, we aimed to take inventory of deep learning networks used in agriculture and conduct experiments to reveal the most efficient ones for weed control. Study objects and methods. We used new advanced algorithms based on deep learning to process data in real time with high precision and efficiency. These algorithms were trained on a dataset containing real images of weeds taken from Moroccan fields. Results and discussion. The analysis of deep learning methods and algorithms trained to detect weeds showed that the Convolutional Neural Network is the most widely used in agriculture and the most efficient in weed detection compared to others, such as the Recurrent Neural Network. Conclusion. Since the Convolutional Neural Network demonstrated excellent accuracy in weed detection, we adopted it in building a smart system for detecting weeds and spraying them in place.Brahim JabirLoubna RabhiNoureddine FalihKemerovo State Universityarticledigital agricultureweed detectionmachine learningdeep learningconvolutional neural network (cnn)recurrent neural network (rnn)Food processing and manufactureTP368-456ENFoods and Raw Materials, Vol 9, Iss 2, Pp 387-396 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	digital agriculture weed detection machine learning deep learning convolutional neural network (cnn) recurrent neural network (rnn) Food processing and manufacture TP368-456
spellingShingle	digital agriculture weed detection machine learning deep learning convolutional neural network (cnn) recurrent neural network (rnn) Food processing and manufacture TP368-456 Brahim Jabir Loubna Rabhi Noureddine Falih RNN- and CNN-based weed detection for crop improvement: An overview
description	Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant classification, fruit counting, pest identification, and weed detection. The latter was the subject of our work. Weeds are harmful plants that grow in crops, competing for things like sunlight and water and causing crop yield losses. Traditional data processing techniques have several limitations and consume a lot of time. Therefore, we aimed to take inventory of deep learning networks used in agriculture and conduct experiments to reveal the most efficient ones for weed control. Study objects and methods. We used new advanced algorithms based on deep learning to process data in real time with high precision and efficiency. These algorithms were trained on a dataset containing real images of weeds taken from Moroccan fields. Results and discussion. The analysis of deep learning methods and algorithms trained to detect weeds showed that the Convolutional Neural Network is the most widely used in agriculture and the most efficient in weed detection compared to others, such as the Recurrent Neural Network. Conclusion. Since the Convolutional Neural Network demonstrated excellent accuracy in weed detection, we adopted it in building a smart system for detecting weeds and spraying them in place.
format	article
author	Brahim Jabir Loubna Rabhi Noureddine Falih
author_facet	Brahim Jabir Loubna Rabhi Noureddine Falih
author_sort	Brahim Jabir
title	RNN- and CNN-based weed detection for crop improvement: An overview
title_short	RNN- and CNN-based weed detection for crop improvement: An overview
title_full	RNN- and CNN-based weed detection for crop improvement: An overview
title_fullStr	RNN- and CNN-based weed detection for crop improvement: An overview
title_full_unstemmed	RNN- and CNN-based weed detection for crop improvement: An overview
title_sort	rnn- and cnn-based weed detection for crop improvement: an overview
publisher	Kemerovo State University
publishDate	2021
url	https://doaj.org/article/76c47661b439482785715b42fe919e86
work_keys_str_mv	AT brahimjabir rnnandcnnbasedweeddetectionforcropimprovementanoverview AT loubnarabhi rnnandcnnbasedweeddetectionforcropimprovementanoverview AT noureddinefalih rnnandcnnbasedweeddetectionforcropimprovementanoverview
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RNN- and CNN-based weed detection for crop improvement: An overview

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