Predicting fruit maturity stage dynamically based on fuzzy recognition and color feature

Xiao, Qingmei; Niu, Wendi; Zhang, Hong

doi:10.1109/icsess.2015.7339210

Cited by 15 publications

(6 citation statements)

References 15 publications

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“…Di seluruh dunia, tomat juga merupakan tanaman hortikultura yang penting, dan merupakan buah yang paling banyak diekspor [3], [4]. Mengetahui tingkat kematangan tomat penting untuk tujuan yang berbeda seperti prioritas transportasi ke pasar dan penyimpanan berdasarkan tahap kematangan tomat [5]. Secara tradisional, tomat diklasifikasikan berdasarkan kematangan fisiologisnya dengan penyortiran manual.…”

Section: Pendahuluanunclassified

Peningkatan HSV dan Haar-Like Feature pada Aplikasi Identifikasi Kematangan Buah Tomat Berbasis Android

Liantoni

Prakisya

Aristyagama

2021

justin

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Tomat adalah buah yang terkenal karena memiliki banyak nutrisi penting dan bermanfaat seperti antioksidan, vitamin C dan A untuk makanan sehari-hari manusia. Memetik tomat dengan tangan merupakan pekerjaan yang berat dan memakan waktu. Karena itu, untuk mengatasi masalah ini, tomat perlu diambil secara otomatis dengan bantuan teknologi. Baru-baru ini otomatisasi panen buah memperoleh popularitas besar. Untuk memandu robot pemanen mengambil buah dengan benar, penting untuk mendeteksi dan menemukan lokasi buah matang merah dengan benar. Maka dibutuhkan aplikasi untuk identifikasi kematangan buah tomat. Dalam penelitian ini, algoritma pendeteksian tomat matang berdasarkan ruang warna HSV (Hue, Saturation, Value) yang ditingkatkan dengan haar-like feature. Metode ini diterapakan pada aplikasi berbasis android. Pada tahap pertama, transformasi HSV digunakan untuk menghilangkan latar belakang dan hanya mendeteksi tomat merah. Kemudian operasi morfologis diterapkan untuk memodifikasi buah yang terdeteksi. Hasil penelitian mampu mendeteksi tomat matang merah dengan peningkatan HSV dan haar-like feature.

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Section: Pendahuluanunclassified

Peningkatan HSV dan Haar-Like Feature pada Aplikasi Identifikasi Kematangan Buah Tomat Berbasis Android

Liantoni

Prakisya

Aristyagama

2021

justin

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“…For example, tomatoes can be harvested in the physiological maturity stage (green), which can complete the ripening after harvest. Therefore, it is necessary to accurately locate and distinguish the maturity of tomatoes for different harvesting purposes [17][18][19]. Zhang et al improved the deep learning-based classification method to classify tomato maturity by inserting two layers of max-pooling in CNN layers.…”

Section: Introductionmentioning

confidence: 99%

Tomato Maturity Classification Based on SE-YOLOv3-MobileNetV1 Network under Nature Greenhouse Environment

Zhao

Wang

et al. 2022

Agronomy

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The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot.

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“…To realize real-time accurate recognition of the maturity of fruits and vegetables, Fadhel and Al-Shamma [18] proposed a field programmable gate array (FPGA) as the parallel hardware structure, aiming to reduce the high time cost of color thresholding and k-means clustering (KMC). Xiao et al [19] predicted the maturation stage of tomato fruits according to surface color, and managed to forecast the surface color variation based on temperature conditions. Cai and Zhao [20] developed a mature fruit recognition technique in natural scenes, which compares the color models of hue-saturation-intensity (HSI) system.…”

Section: Introductionmentioning

confidence: 99%

Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

Chen¹,

Tang²,

Xi³

et al. 2021

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The accurate judgement of fruit maturity is significant for modern agriculture. At present, few scholars have monitored and recognized fruit maturity based on the Internet of things (IoT) and image recognition technology. Therefore, this paper explores the image recognition of fruit maturity in the context of agricultural Internet of things (IoT). Firstly, the single shot multi-box detection (SSD) algorithm was improved for fruit recognition and positioning, and used to determine the size and position the fruits to be recognized. Next, an image fusion algorithm was designed based on improved Laplacian pyramid, which effectively compresses the large fruit monitoring images shot in the same scene. The proposed algorithm was proved feasible and effective through experiments.

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Predicting fruit maturity stage dynamically based on fuzzy recognition and color feature

Cited by 15 publications

References 15 publications

Peningkatan HSV dan Haar-Like Feature pada Aplikasi Identifikasi Kematangan Buah Tomat Berbasis Android

Peningkatan HSV dan Haar-Like Feature pada Aplikasi Identifikasi Kematangan Buah Tomat Berbasis Android

Tomato Maturity Classification Based on SE-YOLOv3-MobileNetV1 Network under Nature Greenhouse Environment

Image Recognition of Modern Agricultural Fruit Maturity Based on Internet of Things

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