A Review on Agricultural Advancement Based on Computer Vision and Machine Learning

Paul, Abriti; Ghosh, Sourav; Das, Amit Kumar; Goswami, Saptarsi; Das, Mita; Sen, Soumya

doi:10.1007/978-981-13-7403-6_50

Cited by 25 publications

(16 citation statements)

References 45 publications

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“…The major purpose of image processing is to improve the quality of images, to identify desired objects, and automate the management of objects. It is usually performed following several steps such as (1) preprocessing, (2) image segmentation, (3) feature extraction based on image representation and description, and (4) classification of objects (Paul et al, 2020). Image preprocessing is used to improve the image quality by suppressing undesired distortions, such as background subtraction, noise removal, polygon shape creation, smoothing, histogram operations to enhance the objects, and so on (Paul et al, 2020 (Kamilaris & Prenafeta-Boldú, 2018;Kawano & Yanai, 2014).…”

Section: Image Processingmentioning

confidence: 99%

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Mishra

Panda

Ramirez

et al. 2021

Comp Rev Food Sci Food Safe

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Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.

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Section: Image Processingmentioning

confidence: 99%

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Mishra

Panda

Ramirez

et al. 2021

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…Agriculture operators are now facing the 'Big Data Analysis' prospect: organize, aggregate and interpret the massive sample size of available digital data with sophisticated algorithms to drive decisions based on data interpretation, prediction, and inference potentially on a global scale (Fan et al 2014;Weersink et al 2018). In addition, the implementation of computer vision science (Paul et al 2020), machine learning (Liakos et al 2018), deep learning (Kamilaris and Prenafeta-Boldú 2018), neural networks (Patil and Vohra 2020), fuzzy logic (Kale and Patil 2019) and artificial intelligence (Jha et al 2019) can reduce human interventions and efforts, optimize inputs and maximize outputs. Moreover, all this information is deeply inserted in the highest level of connectivity that humankind has ever witnessed.…”

Section: Challenges For Further Developmentsmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

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“…The formula used to calculate brightness of each original image is as follow: (14) where r, g and b are the mean of each band in RGB image. Figure 22 shows the change of brightness in the 24 images.…”

Section: Effects Of Different Brightness Conditionsmentioning

confidence: 99%

“…There are two important steps in assessing the FHB disease by using digital images: image segmentation for wheat ears and wheat ear counting [9]- [11]. Through literature research it was found that image processing alone and its integration with machine learning are commonly used to achieve the two steps [12]- [14]. For example, Fernandez-Gallego et al used image filtering technique to complete wheat ear image segmentation and wheat ear counting [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

et al. 2020

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Fusarium head blight (FHB) is one of the most important diseases in wheat worldwide. Evaluation and identification of effective fungicides are essential for control of FHB. However, traditional methods based on the manual disease severity assessment to evaluate the efficacy of fungicides are timeconsuming and laborsome. In this study, we developed a new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs. Enhanced red-green-green (RGG) images were processed from acquired raw red-green-blue (RGB) images of wheat ear samples; the images were transformed in color spaces through K-means clustering for rough segmentation of wheat ears; a random forest classifier was used with features of color, texture, geometry and vegetation index for fine segmentation of disease spots in wheat ears; a newly proposed width mutation counting algorithm was used to count wheat ears; and the disease severity of the wheat ears groups was graded and the efficacy of six fungicides was evaluated. The results show that the segmentation algorithm could segment wheat ears from a complex field background. And the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion. The average counting accuracy of all and diseased wheat ears were 93.00% and 92.64%, respectively, with the coefficients of determination (R 2) of 0.90 and 0.98, and the root mean square error (RMSE) of 10.56 and 7.52, respectively. The new method could accurately assess the diseased levels of wheat eat groups infected by FHB and determine the efficacy of the six fungicides evaluated. The results demonstrate a potential of using digital imaging technology to evaluate and identify effective fungicides for control of the FHB disease in wheat and other crop diseases. INDEX TERMS Fusarium head blight, K-means clustering, random forest, width mutation counting algorithm, pesticide spraying.

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A Review on Agricultural Advancement Based on Computer Vision and Machine Learning

Cited by 25 publications

References 45 publications

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts

Insect pest monitoring with camera-equipped traps: strengths and limitations

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

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