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Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review

IEEE ACCESS 2023年 11卷 171-202页

作者： Elbasi, Ersin Mostafa, Nour AlArnaout, Zakwan Zreikat, Aymen I. Cina, Elda Varghese, Greeshma Shdefat, Ahmed Topcu, Ahmet E. Abdelbaki, Wiem Mathew, Shinu Zaki, Chamseddine Amer Univ Middle East Coll Engn & Technol Kuwait 54200 Kuwait

Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

关键词： Artificial intelligence applications agriculture smart farming Internet of Things sensors machine learning deep learning

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image sensing with multilayer nonlinear optical neural networks

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NATURE PHOTONICS 2023年第5期17卷 408-+页

作者： Wang, Tianyu Sohoni, Mandar M. Wright, Logan G. Stein, Martin M. Ma, Shi-Yuan Onodera, Tatsuhiro Anderson, Maxwell G. McMahon, Peter L. Cornell Univ Sch Appl & Engn Phys Ithaca NY 14850 USA NTT Res Inc Phys & Informat Labs Sunnyvale CA 94303 USA Cornell Univ Kavli Inst Cornell Nanoscale Sci Ithaca NY 14850 USA

A nonlinear optical neural network image sensor based on an image intensifier enables efficient all-optical image encoding for a variety of machine-vision tasks. Optical imaging is commonly used for both scientific and technological applications across industry and academia. In image sensing, a measurement, such as of an object's position or contour, is performed by computational analysis of a digitized image. An emerging image-sensing paradigm relies on optical systems that-instead of performing imaging-act as encoders that optically compress images into low-dimensional spaces by extracting salient features;however, the performance of these encoders is typically limited by their linearity. Here we report a nonlinear, multilayer optical neural network (ONN) encoder for image sensing based on a commercial image intensifier as an optical-to-optical nonlinear activation function. This nonlinear ONN outperforms similarly sized linear optical encoders across several representative tasks, including machine-vision benchmarks, flow-cytometry image classification and identification of objects in a three-dimensionally printed real scene. For machine-vision tasks, especially those featuring incoherent broadband illumination, our concept allows for a considerable reduction in the requirement of camera resolution and electronic post-processing complexity. In general, image pre-processing with ONNs should enable image-sensing applications that operate accurately with fewer pixels, fewer photons, higher throughput and lower latency.

关键词： Applied optics Imaging and sensing

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Evaluation of machine learning in recognizing images of reinforced concrete damage

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MULTIMEDIA TOOLS AND applications 2023年第19期82卷 30221-30246页

作者： Fan, Ching-Lung Republ China Mil Acad Dept Civil Engn 1 Weiwu RdFengshan Kaohsiung 83059 Taiwan

Damage to reinforced concrete (RC) facilities occurs through the process of natural deterioration. machine learning can be employed to effectively identify various damage areas and ensure safety. The performance of machine vision methods depends on image quality. In this study, five image types (Types I-V) with combinations of image deficiencies pertaining to uniform illuminance, uneven illuminance, orthoimage, tilt angle, and image blur were used to evaluate the damage recognition capabilities of maximum likelihood (MLH), support vector machine (SVM), and random forest (RF) methods. Type I images were orthoimages with uniform illuminance, Type ii images were tilted images with uniform illuminance, Type iiI images were orthoimages with uneven illuminance, Type IV images were tilted images with uneven illuminance, and Type V images were tilted, blurred images with uneven illuminance. MLH was most accurate (98.6%) in Type I images, and RF was the least accurate (62.8%) in Type V images. image tilt (in Type ii images) did not diminish the damage recognition capabilities of the three types of machine learning methods (mean accuracy = 97.2%). For tilted images with uneven illuminance (Type IV), a severe expansion effect was produced, reducing the mean accuracy to 70.1%. Type iiI images were recognized with a mean accuracy of 87.1%;uneven illuminance increased the error rate for three classes of damage. By testing various image types, the impact of image quality on the variability of machine learning recognition is understood, and the ability of automated machine learning recognition in the future is improved.

关键词： Computer vision Maximum likelihood Support vector machine Random forest image quality

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An image quality assessment method based on edge extraction and singular value for blurriness

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machine vision AND applications 2024年第3期35卷 37-37页

作者： Zhou, Lei Liu, Chuanlin Yadav, Amit Azam, Sami Karim, Asif Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu Peoples R China Sichuan DOOV Intelligent Cloud Valley Co Ltd Yibin Peoples R China Charles Darwin Univ Fac Sci & Technol Darwin NT 0909 Australia

The automatic assessment of perceived image quality is crucial in the field of image processing. To achieve this idea, we propose an image quality assessment (IQA) method for blurriness. The features of gradient and singular value were extracted in this method instead of the single feature in the traditional IQA algorithms. According to the insufficient size of existing public image quality assessment datasets to support deep learning, machine learning was introduced to fuse the features of multiple domains, and a new no-reference (NR) IQA method for blurriness denoted Feature fusion IQA(Ffu-IQA) was proposed. The Ffu-IQA uses a probabilistic model to estimate the probability of each edge detection blur in the image, and then uses machine learning to aggregate the probability information to obtain the edge quality score. After that uses the singular value obtained by singular value decomposition of the image matrix to calculate the singular value score. Finally, machine learning pooling is used to obtain the true quality score. Ffu-IQA achieves PLCC scores of 0.9570 and 0.9616 on CSIQ and TID2013, respectively, and SROCC scores of 0.9380 and 0.9531, which are better than most traditional image quality assessment methods for blurriness.

关键词： image quality assessment No-reference Blur Gradient Singular value machine learning

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Cloud-based analysis of aerial imagery for unveiling ancient archaeological patterns

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND applications 2025年第1期14卷 1-14页

作者： Fusco, Pietro Amato, Alba Venticinque, Salvatore Univ Campania Luigi Vanvitelli Dept Engn Via Roma 29 I-81031 Aversa Caserta Italy Univ Salerno Dept Comp Sci Via Giovanni Paolo II132 I-84084 Fisciano Salerno Italy Univ Campania Luigi Vanvitelli Dept Polit Sci Viale Ellittico 29 I-81100 Caserta Italy

image processing and artificial intelligence techniques represent new and effective tools for supporting archaeological research to bring ancient finds to light. They can help archaeologists to discover remains that are difficult to identify using traditional approaches. The design and development of such applications, which aim at processing large amounts of data to cover extended areas, requires the use of Cloud paradigms for exploiting Cloud elasticity and scaling with the problem size. This paper presents an original methodology that integrates deep learning, computer vision, and optimization models to identify archaeological remains from aerial images. Results demonstrate how the proposed approach can search for the remains of Centuriation, which is an ancient Roman system for dividing the land over a large area, and evaluate the scalability of a map-reduce implementation in the Cloud.

关键词： Map reduce Semantic segmentation Roman centuriation image processing

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Joint loss function design in diffractive optical neural network classifiers for high power efficiency

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Optics Express 2025年第4期33卷 7307-7320页

作者： Fan, Mengguang Jin, Shuping Gu, Yinwei Zhao, Xiangyue Bai, Ningfeng Wang, Qilong Lu, Changgui School of Electronic Science and Engineering Southeast University Nanjing211189 China

The high speed, wide bandwidth, and parallel processing capabilities of a diffractive optical neural network (DONN) stimulate its applications in computer vision for image recognition and information processing tasks. This paper presents a joint loss function (J-SCE) that combines classification performance and diffractive power efficiency, thereby improving the power efficiency of the DONN classifier from 0.92% to 12.89% while maintaining a classification accuracy of 95.36%. The J-SCE function improves the overall power efficiency of the system by directing energy more effectively toward the target area. Furthermore, the J-SCE function enhances the system’s robustness to noise and overall stability. This work significantly contributes to the application of DONN classifiers in practical image recognition and other information processing scenarios. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Imaging systems Information processing Light matter interactions machine vision Optical computing Optical neural systems

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A review of adaptable conventional image processing pipelines and deep learning on limited datasets

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machine vision AND applications 2024年第2期35卷 25-25页

作者： Muenke, Friedrich Rieken Schuetzke, Jan Berens, Felix Reischl, Markus KIT Inst Automat & Appl Informat IAI Hermann von Helmholtz Pl 1 D-76344 Karlsruhe Baden Wurttembe Germany Univ Appl Sci Ravensburg Weingarten Inst Artificial Intelligence Doggenriedstr D-88250 Weingarten Baden Wurttembe Germany

The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for utilizing both approaches. We introduce a synthetic data generator, which enables us to evaluate the impact of the number of training samples as well as the difficulty and diversity of the dataset. We show that deep learning methods excel when large datasets are available and conventional image processing approaches perform well when the datasets are small and diverse. Since transfer learning is a common approach to work around small datasets, we are specifically assessing its impact and found only marginal impact. Furthermore, we implement the conventional image processing pipeline to enable fast and easy application to new problems, making it easy to apply and test conventional methods alongside deep learning with minimal overhead.

关键词： Deep learning Conventional image processing Comparison Synthetic data

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Flexible and Transportable Illumination Control System for machine vision applications 10th

Flexible and Transportable Illumination Control System for M...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent Systems

作者： Gusic, Muhamed Sokic, Emir Univ Sarajevo Dept Automat Control & Elect Fac Elect Engn Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

During the development of machine vision setups for industrial applications, namely automatic visual inspection systems, the type of required lighting and correct illumination position and intensity are often not known in advance. In order to solve a specific machine vision task, an engineer needs to propose and implement a suitable image processing algorithm and design the accompanying lighting system. Testing different illumination concepts, especially "in the field", using off-the-shelf lighting systems is time consuming. The paper presents a custom-made, cost effective, transportable and flexible system which may be used for testing different illumination techniques and simultaneously control multiple lighting modules. In addition to the hardware part, the developed software used for control the system is presented in the paper. The proposed device usefulness is demonstrated by solving a suitable multi-light digital image processing task.

关键词： machine vision visual inspection illumination control system embedded systems digital image processing

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Printed circuit board inspection using computer vision

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MULTIMEDIA TOOLS AND applications 2024年第6期83卷 16363-16375页

作者： Rajesh, A. Jiji, G. Wiselin Indian Space Res Org Vikram Sarabhai Space Ctr Thiruvananthapuram India Dr Sivanthi Aditanar Coll Engn Dept Comp Sci & Engn Tiruchendur India

The inspection of electronic components, especially printed circuit boards (PCBs), has greatly benefited from the advancements in computer vision technology. With the miniaturization of electronic components, defects on PCBs are now often found in smaller or micro-sized forms. This poses a significant challenge for automated optical inspection methods to effectively detect and identify such small objects. The primary objective of this study is to address the issue of fault detection in printed circuit boards (PCBs). To achieve this, the study employs various image processing techniques to carry out the inspection process. These image processing operations play a crucial role in preparing the images for defect analysis. Once the image processing operations are completed, the study proceeds to classify the identified defects in the segmented regions using a support vector machine (SVM) classifier. The SVM classifier is trained to categorize the defects based on the extracted features and their respective class labels. This classification step plays a critical role in accurately identifying and characterizing the detected defects. To evaluate the effectiveness of this study, a comparison is made with earlier works in the field. This allows for a comprehensive assessment of the proposed methodology and its performance in comparison to existing approaches. By benchmarking against previous works, the study provides valuable insights into the advancements and improvements achieved in PCB defect detection.

关键词： Feature extraction Classification PCB inspection

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Congruent Feature Selection Method to Improve the Efficacy of machine Learning-Based Classification in Medical image processing

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： Computer vision feature selection machine learning region detection texture analysis image classification medical images

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