Improving efficiency of organic farming by using a deep learning classification approach

Knöll, Florian; Czymmek, Vitali; Poczihoski, Sascha; Holtorf, Tim; Hußmann, Stephan

doi:10.1016/j.compag.2018.08.032

Cited by 45 publications

(16 citation statements)

References 20 publications

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“…Guo et al [ 92 ] utilized 18 color features (r, g, b; Y, Cb, Cr; H, S, L; H, S, V; L*, a*, b*; L*, u*, v*), which were defined in 6 color spaces (RGB, YCbCr, HSL, HSV, CIEL*a*b*, and CIEL*u*v*). Knoll et al [ 93 ] and Jin [ 94 ] also utilized different color spaces.…”

Section: Traditional Machine Learning Weed Detection Methodsmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

174

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Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.

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Section: Traditional Machine Learning Weed Detection Methodsmentioning

confidence: 99%

Review of Weed Detection Methods Based on Computer Vision

Chen

Zhao

et al. 2021

Sensors

174

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“…In [ 50 ], a weed detection approach is implemented based on HSV color space for the image segmentation and a CNN for the classification to weed and carrots. Apparently, due to the great difference of color between carrots and weeds, this method based on color performs well.…”

Section: Plant/fruit Health Protection and Disease Detection Approachesmentioning

confidence: 99%

Machine Vision Systems in Precision Agriculture for Crop Farming

et al. 2019

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Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

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“…Veeramani et al [31] and Altuntaş et al [32] applied deep convolutional networks (CNN) for sorting haploid maize seeds. Knoll et al [33] proposed a self-learning CNN, to distinguish individual classes of plants using the visual sensor data in real-time. Häni et al [34], Tian et al [35], Gené-Mola et al [36], and Kang and Chen [37] proposed detection and counting methods for apples in orchards.…”

Section: B Plant Classificationmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

100

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Improving efficiency of organic farming by using a deep learning classification approach

Cited by 45 publications

References 20 publications

Review of Weed Detection Methods Based on Computer Vision

Review of Weed Detection Methods Based on Computer Vision

Machine Vision Systems in Precision Agriculture for Crop Farming

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

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