Automatic fruit count on coffee branches using computer vision

Ramos, P.J.; Prieto, Flavio; Montoya, Esther Cecilia; Oliveros, Carlos Eugenio

doi:10.1016/j.compag.2017.03.010

Cited by 132 publications

(67 citation statements)

References 7 publications

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“…Gong et al (2013) presented an android-based application with the aim of predicting the yield of citrus orchards by first acquiring and storing the images and later processing them. The colour information captured by mobile devices was used to study the structure of coffee branches and determine the number of fruits by Ramos et al, (2017) and Avendano et al, (2017).…”

mentioning

confidence: 99%

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

Cubero

Albert

Prats-Moltalbán

et al. 2018

Biosystems Engineering

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mentioning

confidence: 99%

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

Cubero

Albert

Prats-Moltalbán

et al. 2018

Biosystems Engineering

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“…Dorj et al develop a watershed segmentation based method to detect citrus in HSV space [5]. Ramos et al use contour analysis on superpixel over-segmentation result to fit ellipses for counting coffee fruits on branches [6]. Roy et al develop a two-step apple counting method which first uses RGB-based oversegmentation for fruit area proposal, then estimates fruit count by fitting a clustering model with different center numbers [7].…”

Section: Related Workmentioning

confidence: 99%

Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association

Liu

Chen

Liu

et al. 2019

IEEE Robot. Autom. Lett.

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We present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera, and achieves counting performance comparable to a state-of-the-art fruit counting system that utilizes an expensive sensor suite including a monocular camera, LiDAR and GPS/INS on a mango dataset. Our pipeline begins with a fruit and tree trunk detection component that uses state-of-the-art convolutional neural networks (CNNs). It then tracks fruits and tree trunks across images, with a Kalman Filter fusing measurements from the CNN detectors and an optical flow estimator. Finally, fruit count and map are estimated by an efficient fruit-as-feature semantic structure from motion (SfM) algorithm which converts 2D tracks of fruits and trunks into 3D landmarks, and uses these landmarks to identify double counting scenarios. There are many benefits of developing such a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.Index Terms-Robotics in agriculture and forestry, deep learning in robotics and automation, visual tracking, mapping, object detection, segmentation and categorization.

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“…Still, many approaches for enhanced vineyard management techniques are introduced, which involve automatic yield estimation [17][18][19], grape quality evaluation [20], and grapevine variety identification [21]. In a relevant approach, Ramos et al [22] proposed a method of calculating the number of fruits on a coffee branch in the field. Their system categorized fruits as either harvestable or non-harvestable through identifying their number, weight, and maturation percentage non destructively.…”

Section: Utilization Of Artificial Intelligencementioning

confidence: 99%

A Collaborative Pilot Platform for Data Annotation and Enrichment in Viticulture

2019

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It took some time indeed, but the research evolution and transformations that occurred in the smart agriculture field over the recent years tend to constitute the latter as the main topic of interest in the so-called Internet of Things (IoT) domain. Undoubtedly, our era is characterized by the mass production of huge amounts of data, information and content deriving from many different sources, mostly IoT devices and sensors, but also from environmentalists, agronomists, winemakers, or plain farmers and interested stakeholders themselves. Being an emerging field, only a small part of this rich content has been aggregated so far in digital platforms that serve as cross-domain hubs. The latter offer typically limited usability and accessibility of the actual content itself due to problems dealing with insufficient data and metadata availability, as well as their quality. Over our recent involvement within a precision viticulture environment and in an effort to make the notion of smart agriculture in the winery domain more accessible to and reusable from the general public, we introduce herein the model of an aggregation platform that provides enhanced services and enables human-computer collaboration for agricultural data annotations and enrichment. In principle, the proposed architecture goes beyond existing digital content aggregation platforms by advancing digital data through the combination of artificial intelligence automation and creative user engagement, thus facilitating its accessibility, visibility, and re-use. In particular, by using image and free text analysis methodologies for automatic metadata enrichment, in accordance to the human expertise for enrichment, it offers a cornerstone for future researchers focusing on improving the quality of digital agricultural information analysis and its presentation, thus establishing new ways for its efficient exploitation in a larger scale with benefits both for the agricultural and the consumer domains.

show abstract

Automatic fruit count on coffee branches using computer vision

Cited by 132 publications

References 7 publications

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association

A Collaborative Pilot Platform for Data Annotation and Enrichment in Viticulture

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