2017 International Joint Conference on Neural Networks (IJCNN) 2017
DOI: 10.1109/ijcnn.2017.7966067
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A hybrid machine learning approach to automatic plant phenotyping for smart agriculture

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Cited by 38 publications
(28 citation statements)
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“…Nevertheless, methods used for flower detection can be selected based on the appearance and color of flowers and desired accuracy. [17,20,39] Current study and [40] SVM in [15,16,23] CNN in [18,24] Crops Apple, peach, pea, lesquerella, canola, camelina, chickpea…”
Section: Methods Of Flower Detectionmentioning
confidence: 99%
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“…Nevertheless, methods used for flower detection can be selected based on the appearance and color of flowers and desired accuracy. [17,20,39] Current study and [40] SVM in [15,16,23] CNN in [18,24] Crops Apple, peach, pea, lesquerella, canola, camelina, chickpea…”
Section: Methods Of Flower Detectionmentioning
confidence: 99%
“…Flower detection has been tested on plant species such as cereals (rice, wheat, and corn), legumes (soybean), tree fruits (apple and citrus), oilseeds (lesquerella and canola), fibers (cotton), and ornamentals (rose) [15][16][17][18][19][20][21][22][23][24]. In literature, image-based detection of tangerine and lesquerella flowers showed good relationship between manual and image-based detection (R 2 = 0.91 and 0.87-0.91, respectively) [19,20].…”
Section: Introductionmentioning
confidence: 99%
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“…Differing from the so-called single methods in Section 2.1, a combined method here means that several methods are used together in a parallel or serial structure. For example, Yahata et al [60] combined machine learning techniques to construct sensing methods in an agricultural cyber-physical system, in which big data of agricultural plants and environmental information (e.g., temperature, humidity, solar radiation, soil condition, etc.) were analyzed to mine useful rules for appropriate cultivation.…”
Section: Combined Methodsmentioning
confidence: 99%
“…Yahata, So, et al [18] has explained how a hybrid machine learning approach can be used for automatic plant phenotyping in smart agriculture. This research has considered 2 image sensing methods, one is for flower detection and the other one is for seedpod detection.…”
Section: Increasing the Productivity Of Agriculture Using Machine Leamentioning
confidence: 99%