Sweet pepper maturity evaluation

Harel, Ben; Kurtser, Polina; Parmet, Yisrael; Edan, Yael

doi:10.1017/s2040470017001236

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The best total harvesting efficiency was obtained at a speed of 400 rpm. However, operating the equipment at this speed resulted in overall equipment vibration [17].…”

Section: Trendsmentioning

confidence: 99%

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Zhang

Cai²

2022

AJRCS

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Since the reform and opening up, the mechanization level of China's agricultural production has been significantly improved. Intelligent mechanization of agricultural production is an inevitable trend for future social development. It is based on intelligent equipment, with data and knowledge as the core elements to enable the deep integration of modern science and technology with agriculture. This enables digital perception, intelligent decision making, and precise operation of the whole agricultural production process, further improving labor productivity, land output rate and resource utilization rate. This paper introduces the development status of agricultural production in China and abroad with pepper harvesting, analyzes the necessity of changing China's agricultural production to intelligent mechanized production, summarizes the opportunities and challenges in intelligent mechanization, and accordingly puts forward the development direction and development route of China's agricultural production. To promote the transformation of China's agricultural production mode from mechanization to wisdom, this paper puts forward countermeasure suggestions such as strengthening the construction of agricultural machinery equipment R&D and innovation system, strengthening the construction of agricultural machinery promotion system, strengthening the construction of agricultural machinery socialization service system, and accelerating the construction of wisdom agriculture demonstration area.

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“…The best total harvesting efficiency was obtained at a speed of 400 rpm. However, operating the equipment at this speed resulted in overall equipment vibration [17].…”

Section: Trendsmentioning

confidence: 99%

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Zhang

Cai²

2022

AJRCS

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“…These heterogeneous ripening patterns hinder the possibility to determine maturity accurately from a single viewpoint since the information from this single sideview might be misleading. Although a single bottom-view can be used to differentiate between mature and immature peppers [29,30], a viewpoint directly below the pepper (facing upwards) is most likely not applicable for robotic harvesting because this viewpoint is often occluded by leaves [31]. Furthermore, it is not efficient since it requires the robot to move the camera from the bottom of the pepper all the way up to the peduncle to harvest the pepper when it is mature.…”

Section: Maturity Classificationmentioning

confidence: 99%

“…A total of 69 red and 70 yellow sweet peppers were harvested in a commercial greenhouse in Kmehin, Israel, on 18 November 2019. The peppers were manually classified into maturity classes 2-4 (Table 1, Figure 2), which are defined in Harel et al [30], by manually observing all sides of the pepper. Peppers belonging to class 2 (50-95% green) are considered as immature and peppers belonging to classes 3 (50-95% colored) and 4 (more than 95% colored) are considered mature.…”

Section: Data Collectionmentioning

confidence: 99%

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

et al. 2022

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This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs.

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“…These algorithms rely mainly on previous work in indoor conditions where detailed 3D plant models can be extracted using hand‐held 3D scanners (Schunck et al, 2021), acquiring data from multiple viewpoints. Despite previous work showing that employing multiple viewpoints can significantly improve precision (i.e., Harel et al, 2016; Kurtser & Edan, 2018b), most outdoor algorithms rely on pointclouds acquired from a single location. This can be attributed to a working assumption often voiced in the field that state‐of‐the‐art registration algorithms generally fail to provide accurate registration results for the noisy outdoor sensory data acquired from RGB‐D cameras and the dense and repetitive soft dynamic foliage present in the agricultural domain.…”

Section: Introductionmentioning

confidence: 98%

NDT‐6D for color registration in agri‐robotic applications

Gupta,

Lilienthal,

Andreasson

et al. 2023

Journal of Field Robotics

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Registration of point cloud data containing both depth and color information is critical for a variety of applications, including in-field robotic plant manipulation, crop growth modeling, and autonomous navigation. However, current state-ofthe-art registration methods often fail in challenging agricultural field conditions due to factors such as occlusions, plant density, and variable illumination.To address these issues, we propose the NDT-6D registration method, which is a color-based variation of the Normal Distribution Transform (NDT) registration approach for point clouds. Our method computes correspondences between pointclouds using both geometric and color information and minimizes the distance between these correspondences using only the three-dimensional (3D) geometric dimensions. We evaluate the method using the GRAPES3D data set collected with a commercial-grade RGB-D sensor mounted on a mobile platform in a vineyard. Results show that registration methods that only rely on depth information fail to provide quality registration for the tested data set. The proposed color-based variation outperforms state-of-the-art methods with a root mean square error (RMSE) of 1.1-1.6 cm for NDT-6D compared with 1.1-2.3 cm for other color-information-based methods and 1.2-13.7 cm for noncolor-information-based methods. The proposed method is shown to be robust against noises using the TUM RGBD data set by artificially adding noise present in an outdoor scenario. The relative pose error (RPE) increased ~14% for our method compared to an increase of ~75% for the best-performing registration method. The obtained average accuracy suggests that the NDT-6D registration methods can be used for in-field precision agriculture applications, for example, crop detection, size-based maturity estimation, and growth modeling.

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Sweet pepper maturity evaluation

Cited by 7 publications

References 14 publications

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Research Status and Development Trend of Intelligent Mechanized Pepper Harvesting

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

NDT‐6D for color registration in agri‐robotic applications

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