Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Patrício, Diego; Rieder, Rafael

doi:10.1016/j.compag.2018.08.001

Cited by 702 publications

(414 citation statements)

References 55 publications

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“…5) Artificial Intelligence (AI): AI has been employed in smart systems over a long period of time [31], being the science of creating intelligent machines to facilitate everyday life [32]. AI covers many areas, including computer vision, data mining, deep learning, image processing and neural networks [16,33]. AI technologies are now emerging to assist and improve efficiency and tackle many of the challenges facing the agricultural industry, including soil health, crop yield and herbicide-resistance.…”

Section: ) Mobile Computing (Mc)mentioning

confidence: 99%

“…AI therefore has significant potential to address the urgent challenges faced by traditional agriculture. There has, over previous decades, been considerable research and application of AI, including in: (a) smart agriculture; (b) robotics; (c) agricultural optimization management; (d) automation; (e) agricultural expert systems; (f) agricultural knowledge-based systems; and (g) decision support systems [16].…”

Section: Introductionmentioning

confidence: 99%

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Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Alreshidi¹

2019

IJACSA

119

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The Internet of Things (IoT) and Artificial Intelligence (AI) have been employed in agriculture over a long period of time, alongside other advanced computing technologies. However, increased attention is currently being paid to the use of such smart technologies. Agriculture has provided an important source of food for human beings over many thousands of years, including the development of appropriate farming methods for different types of crops. The emergence of new advanced IoT technologies has the potential to monitor the agricultural environment to ensure high-quality products. However, there remains a lack of research and development in relation to Smart Sustainable Agriculture (SSA), accompanied by complex obstacles arising from the fragmentation of agricultural processes, i.e. the control and operation of IoT/AI machines; data sharing and management; interoperability; and large amounts of data analysis and storage. This study firstly, explores existing IoT/AI technologies adopted for SSA and secondly, identifies IoT/AI technical architecture capable of underpinning the development of SSA platforms. As well as contributing to the current body of knowledge, this research reviews research and development within SSA and provides an IoT/AI architecture to establish a Smart, Sustainable Agriculture platform as a solution.

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Section: ) Mobile Computing (Mc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Alreshidi¹

2019

IJACSA

119

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“…• To obtain the common vector, a com , the reference vector is projected onto the orthonormal basis and a difference vector a diff is formed, which is represented in Eqn (7). Once difference vector is subtracted from the reference vector, the common vector, a com , would be modeled as shown in Eqn (8).…”

Section: Common Vector Approach-based Fusionmentioning

confidence: 99%

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

2019

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Background The sustainable management of agricultural resources requires the integration of cutting‐edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible‐near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. Results After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. Conclusion The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry

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“…Visual perception apparatuses (VPAs) are ubiquitous more than ever and play important roles in almost every aspect of modern civilization . They form the foundation for autonomous vehicle control and safety systems and of artificial eyes in industrial fields such as pharmaceuticals, semiconductors, and even agriculture . Another important milestone in this field was marked by the development of bionic eyes for the visually impaired .…”

Section: Introductionmentioning

confidence: 99%

An Optoneuronic Device with Realistic Retinal Expressions for Bioinspired Machine Vision

Berco

Ang

Zhang

2019

Advanced Intelligent Systems

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Machine vision systems rely on communication between cameras and processor modules to capture and analyze visual information. This arrangement renders them as bulky and inefficient in terms of speed and power dissipation for futuristic big data applications that involve artificial intelligence algorithms. An apparatus able to imitate the operation of biologic eyes and function as a standalone platform would therefore present the next evolutional step in machine visual perception. Neuromorphic computing is an alternative approach to the Von Neumann architecture that carries the potential for implementing such intelligent cameras. In this regard, artificial synaptic devices have been widely used in recent years to construct hardware‐based neural networks mainly due to their adjustable electric parameters. Herein, a bioinspired, hybrid electrophotonic responsive neuronic device that mimics the combined functionality of retinal cones and bipolar cells is demonstrated. Under illumination, it features a hyperpolarization‐like current response in an OFF state and a complementary depolarized reaction when toggled to an ON state. Furthermore, electrical pulsing done in conjunction with light stimulation can emulate the horizontal cell neurotransmitter release in center‐surround biologic configurations. These devices may thus serve as building blocks for advanced visual systems, integrating self‐healing sensory and neuromorphic computing into an artificial cognitive retina.

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Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

Cited by 702 publications

References 55 publications

Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence (AI)

Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples

An Optoneuronic Device with Realistic Retinal Expressions for Bioinspired Machine Vision

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