Advancements in satellite image classification : methodologies, techniques, approaches and applications

Kamga, G. A. Fotso; Bouchet, L.; Akram, Tallha; Mbom, A. Mengue; Naqvi, Syed Rameez; Bouroubi, Yacine

doi:10.1080/01431161.2021.1954261

Cited by 24 publications

(6 citation statements)

References 363 publications

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“…Highresolution satellite imagery enables farmers to monitor large expanses of land efficiently, identifying areas that may require specific attention, such as pest infestations or nutrient deficiencies. The continuous advancements in satellite technology have enhanced the temporal and spatial resolution, making it an integral tool for precision agriculture (Fotso Kamga, et al, 2021). Unmanned aerial vehicles, commonly known as drones, have emerged as versatile tools for precision agriculture.…”

Section: Technologies In Crop Monitoringmentioning

confidence: 99%

AI in precision agriculture: A review of technologies for sustainable farming practices

Adebunmi Okechukwu Adewusi,

Onyeka Franca Asuzu,

Temidayo Olorunsogo

et al. 2024

World J. Adv. Res. Rev.

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Precision agriculture, facilitated by advancements in Artificial Intelligence (AI), has emerged as a transformative paradigm in modern farming. This review comprehensively examines the integration of AI technologies in precision agriculture to enhance sustainability and optimize farming practices. The paper synthesizes recent research and developments in AI applications, covering key areas such as crop monitoring, resource management, decision support systems, and automation. The adoption of AI-driven techniques, including machine learning, computer vision, and sensor technologies, is reshaping traditional farming methods by providing farmers with real-time data and actionable insights. Crop monitoring applications utilize satellite imagery, drones, and ground-based sensors to assess plant health, detect diseases, and optimize irrigation strategies. AI-driven decision support systems empower farmers to make informed choices based on data-driven predictions, weather forecasts, and historical patterns, contributing to resource-efficient practices and minimizing environmental impact. Resource management is a critical aspect of sustainable farming, and AI plays a pivotal role in optimizing the use of water, fertilizers, and pesticides. Smart irrigation systems, enabled by AI algorithms, ensure precise and efficient water distribution, reducing water wastage and promoting water conservation. AI-driven analysis of soil conditions helps farmers tailor fertilization practices, enhancing nutrient utilization and minimizing environmental runoff. The review also explores the role of AI in automating farming operations through robotics and autonomous vehicles. These technologies not only alleviate labor shortages but also improve efficiency in planting, harvesting, and crop maintenance. Additionally, the integration of AI fosters connectivity in agriculture, enabling seamless communication between devices, sensors, and farming equipment. As precision agriculture continues to evolve, the review highlights challenges and future prospects. Ethical considerations, data security, and the digital divide in rural areas are among the challenges that need attention. Moreover, the paper discusses potential avenues for further research, emphasizing the need for interdisciplinary collaboration to address the complex issues associated with the sustainable implementation of AI in precision agriculture. This review provides a comprehensive overview of the transformative impact of AI in precision agriculture, offering insights into current technologies, challenges, and future directions. The integration of AI not only enhances productivity and efficiency but also contributes to the long-term sustainability of farming practices, ensuring food security in the face of a growing global population.

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Section: Technologies In Crop Monitoringmentioning

confidence: 99%

AI in precision agriculture: A review of technologies for sustainable farming practices

Adebunmi Okechukwu Adewusi,

Onyeka Franca Asuzu,

Temidayo Olorunsogo

et al. 2024

World J. Adv. Res. Rev.

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“…These features are obtainable attributes or properties of objects and scenes [121]. They are computed from original bands or a combination of bands [122]. They include spectral features, textural features, linear transformations, multisensors and multitemporal images.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

Duarte

Borralho²,

Cabral

et al. 2022

Forests

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Unmanned aerial vehicles (UAVs) are platforms that have been increasingly used over the last decade to collect data for forest insect pest and disease (FIPD) monitoring. These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data. The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring. A systematic review was performed using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol. We reviewed the full text of 49 studies published between 2015 and 2021. The parameters examined were the taxonomic characteristics, the type of UAV and sensor, data collection and pre-processing, processing and analytical methods, and software used. We found that the number of papers on this topic has increased in recent years, with most being studies located in China and Europe. The main FIPDs studied were pine wilt disease (PWD) and bark beetles (BB) using UAV multirotor architectures. Among the sensor types, multispectral and red–green–blue (RGB) bands were preferred for the monitoring tasks. Regarding the analytical methods, random forest (RF) and deep learning (DL) classifiers were the most frequently applied in UAV imagery processing. This paper discusses the advantages and limitations associated with the use of UAVs and the processing methods for FIPDs, and research gaps and challenges are presented.

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“…The initial source image is constructed using the low-pass filter L and the high-pass filter of the resulting image for row upsampling and filtering and the summation of all matrices. By examining the saliency type focusing on bottom-up, the images can be classified into two categories, spatial domain models and transform domain models, depending on whether they are transformed in the frequency domain [ 21 ]. The so-called spatial domain saliency models process the image directly in the spatial domain and thus detect the salient targets or regions of interest in the image.…”

Section: Fuzzy Support Tensor Machine Adaptive Image Classification F...mentioning

confidence: 99%

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

Shi

2022

Computational Intelligence and Neuroscience

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Computer vision is one of the hottest research directions in artificial intelligence at present, and its research goal is to give computers the ability to perceive and cognize their surroundings from a single image. Image recognition is an important research direction in the field of computer vision, which has important research significance and application value in industrial applications such as video surveillance, biometric identification, unmanned vehicles, human-computer interaction, and medical image recognition. In this article, we propose an end-to-end, pixel-to-pixel IoT-oriented fuzzy support tensor product adaptive image classification method. Considering the problem that traditional support tensor product classification methods are difficult to directly produce pixel-to-pixel classification results, the research is based on the idea of inverse convolution network design, which directly outputs dense pixel-by-pixel classification results for images to be classified of arbitrary size to achieve true end-to-end and pixel-to-pixel high-score image classification and improve the efficiency of support tensor product models for high-score image classification on a pixel-by-pixel basis. Moreover, considering that network supervised classification training using deep learning requires a large amount of labeled data as true values and obtaining a large number of labeled data sources is a difficult problem in the field of image classification, this article proposes using a large amount of unlabeled high-resolution remote sensing images for learning generic structured features through unsupervised to assist the labeled high-resolution remote sensing images for better-supervised feature extraction and classification training. By finding a balance between generic structural feature learning of images and differentiated feature learning related to the target class, the dependence of supervised classification on the number of labeled samples is reduced, and the network robustness of the support tensor product algorithm is improved under a small number of labeled training samples.

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Advancements in satellite image classification : methodologies, techniques, approaches and applications

Cited by 24 publications

References 363 publications

AI in precision agriculture: A review of technologies for sustainable farming practices

AI in precision agriculture: A review of technologies for sustainable farming practices

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

Fuzzy Support Tensor Product Adaptive Image Classification for the Internet of Things

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