Opportunities for Artificial Intelligence in Precision Agriculture Using Satellite Remote Sensing

Asmae, Dakir; Barramou, Fatimazahra; Alami, Omar Bachir

doi:10.1007/978-3-030-80458-9_8

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…Remote sensing image refers to films or photos that record the size of electromagnetic waves of various ground objects, mainly divided into aerial photos [84] and satellite photos [85]. Remote sensing imaging methods mainly include aerial photography, aerial scanning, and microwave radar.…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Breeders face the challenge of integrating sensor technologies into their programs to collect relevant data for genotype-phenotype associations. This requires collaboration between breeders, agronomists, and data scientists to ensure the collection of accurate and high-quality data that can inform breeding decisions and the development of targeted traits (Qiao et al, 2023;Araus et al, 2022;Dakir;Barramou;Alami, 2022).…”

Section: Plant Breeding In the Era Of Precision Agriculturementioning

confidence: 99%

“…This data-driven approach enhances the precision and efficiency of plant breeding, enabling the selection of superior varieties for precision agriculture systems (Bhakta;Phadikar;Majumder, 2019. Qiao et al, 2019Dakir;Barramou;Alami, 2022).…”

Section: Plant Breeding In the Era Of Precision Agriculturementioning

confidence: 99%

Rethinking plant breeding and seed systems in the era of exponential changes

Lopes

2023

Ciênc. agrotec.

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The article stresses the need to reassess plant breeding and seed systems in our current era, characterized by rapidly shifting paradigms propelled by technological and scientific progress. It underscores the importance of incorporating cutting-edge technologies, fostering interdisciplinary collaboration, and implementing forward-looking policies to meet evolving societal and consumer demands while aligning with the Sustainable Development Goals (SDGs). Advancements in genetic editing, high-throughput technologies, precision breeding, and digital agriculture are transforming plant breeding, offering unprecedented capabilities to develop desirable traits, comprehend gene-environment interactions, and enhance crop efficiency. However, responsible innovation that considers ethical implications and regulatory frameworks remains essential in this context. Effective collaboration and engagement of stakeholders, including breeders, farmers, policymakers, and consumers, are vital for navigating the challenges and opportunities presented by this era of exponential change. Inclusive and multidisciplinary dialogues can harness diverse perspectives and experiences, contributing to a sustainable and resilient future for plant breeding and seed production. Ensuring access to diverse, high-quality seeds is crucial, especially in developing nations. Investments in seed infrastructure, capacity building, and farmer-centric approaches empower farmers and bolster seed security. In conclusion, the article underscores the significance of reimagining plant breeding and seed systems by embracing technology, promoting collaboration, and aligning strategies with the SDGs. By harnessing the potential of exponential changes, the envisioned future involves plant breeding and seed production that effectively address evolving societal and consumer needs, thereby contributing to agricultural resilience, global food security and sustainability.

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“…Many recent technics and systems aimed to ensure the sustainable development of agriculture [2] and new concepts were developed like precision agriculture [3]. The recent revolution of digital technologies has radically changed agricultural management, like the employment of geospatial technologies, remote sensing data [4], and artificial intelligence [5], [6]. This study proposes a new model to show the contribution of machine learning algorithms in the identification of crop types using both optical and radar satellite images with time series of different vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

Towards a Machine Learning-based Model for Automated Crop Type Mapping

DAKIR¹,

BARRAMOU²,

ALAMI³

2023

IJACSA

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In the field of smart farming, automated crop type mapping is a challenging task to guarantee fast and automatic management of the agricultural sector. With the emergence of advanced technologies such as artificial intelligence and geospatial technologies, new concepts were developed to provide realistic solutions to precision agriculture. The present study aims to present a machine learning-based model for automated crop-type mapping with high accuracy. The proposed model is based on the use of both optical and radar satellite images for the classification of crop types with machine learning-based algorithms. Random Forest and Support Vector Machine, were employed to classify the time series of vegetation indices. Several indices extracted from both optical and radar data were calculated. Harmonical modelization was also applied to optical indices, and decomposed into harmonic terms to calculate the fitted values of the time series. The proposed model was implemented using the geospatial processing services of Google Earth Engine and tested with a case study with about 147 satellite images. The results show the annual variability of crops and allowed performing classifications and crop type mapping with accuracy that exceeds the performances of the other existing models.

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Opportunities for Artificial Intelligence in Precision Agriculture Using Satellite Remote Sensing

Cited by 9 publications

References 34 publications

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Rethinking plant breeding and seed systems in the era of exponential changes

Towards a Machine Learning-based Model for Automated Crop Type Mapping

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