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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan muhammad Khalid Khan muhammad ayub latif muhammad Naveed muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Quantifying Effects of Photoperiod, Temperature and Humidity on Flowering Initiation in Basmati Rice Lines

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American Journal of Plant Sciences 2019年第6期10卷 893-903页

作者： Tahir latif Zulqarnain Haider muhammad Ramzan muhammad Akhter Sadia Gull Abid Mahmood Awais Riaz Rana Ahsen Raza Khan Rice Research Institute Lahore Pakistan Pakistan Agriculture Research Council Kala Shah Kaku Pakistan Yangzhou University Yangzhou China ayub Agricultural Research Institute Faisalabad Pakistan

The results suggested that all the pure Basmati lines or genotypes initiate flowering within a specific range of day length duration and temperature. Delaying the transplanting date did not significantly affect the flowering time of studied Basmati lines. Maximum threshold of daily minimum temperature (MT) was 24°C during both the years. Likewise, maximum threshold of daily day-length (DL) durations was 12 hours and 18 minutes during both the study years. Results showed that the flowering was maximum when the MT and DL values were optimum as depicted by peak of the trend lines, where maximum lines flowered. Maximum flowering initiations took place when DL was about 11 hours and 15 minutes indicating Basmati as short day plant which flowers maximum when day lengths are shorter and dark or night duration is longer. During 2013, 1st date trial showed strong relationship between flowering initiation with dates of flowering during the flowering period as depicted by higher regression (R2 = 0.837) values. In 2nd date trial during the same year, the value is lower (R2 = 0.513) depicting less relationship of flowering initiation with the dates of flowering. The same trend was observed in 2014 trial. In 1st date trial, strong relationship between flowering initiation with dates of flowering during the flowering period as depicted by higher regression (R2 = 0.864) values, was observed. In 2nd date trial during the same year, the value is lower (R2 = 0.1789) depicting very poor relationship of flowering initiation with the dates of flowering, and the same trend (R2 = 0.0544) in 3rd date of trial.

关键词： Basmati Rice Flowering Photoperiod Temperature Humidity

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