Machine vision system for the automatic segmentation of plants under different lighting conditions

Sabzi, Sajad; Abbaspour‐Gilandeh, Yousef; Javadikia, Hossein

doi:10.1016/j.biosystemseng.2017.06.021

Cited by 36 publications

(15 citation statements)

References 22 publications

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“…It has been tested using different configurations of the camera and under different environmental conditions, especially outdoors where the image processing is always more complex due the changing lighting conditions (Sabzi et al, 2017;Sengupta and Lee, 2014).…”

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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“…The aforementioned drawbacks, i.e., different plant/fruit sizes and varying illuminance, are addressed in [ 20 ]. The intensity of sunlight changes from morning to night, inducing change in color features.…”

Section: Plant and Fruit 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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“…Lee et al [15] developed a real-time machine vision system, which was only capable of correctly detecting 47.6% of weeds with 24.2% oversprayed tomato plants in outdoor field conditions. Sabzi et al [16] proposed a machine vision system based on hybrid artificial neural network-harmony search classifiers for automatic segmentation of plants under different illumination conditions and claimed it could be applied under all field applications with higher accuracies. However, reliable studies have not been conducted using mixed canopy conditions that are common in agricultural fields.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

et al. 2020

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Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.

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Machine vision system for the automatic segmentation of plants under different lighting conditions

Cited by 36 publications

References 22 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

Machine Vision Systems in Precision Agriculture for Crop Farming

Real-Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

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