Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility

Lavanya, K.; Subalakshmi, R.; Thirugnanam, Tamizharasi; Jane, Lydia; Victor, Akila

doi:10.12694/scpe.v23i4.2031

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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“…The extracted endmembers relate to certain soil constituents at specified wavelengths. For a variety of uses, such as crop detection and tracking in agriculture utilizing UAV-based hyperspectral imaging, the NFINDR technique has shown to be excellent for unmixing hyperspectral data efficiently on the computer [21].…”

Section: B Spectral Unmixing Using Nfindr Algorithmmentioning

confidence: 99%

Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning

Devi,

Ansari

et al. 2023

IJACSA

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The optimization of crop yield projections has arisen as a major problem in modern agriculture, due to the increasing demand for food supply and the necessity for effective resource management. Precision and scalability are hampered by the limits associated with conventional agricultural production prediction techniques, which mostly rely on observations and simple data sources. While methods like random forest (RF) and K-nearest neighbors (KNN) are widely used, their reliance on personal assessments and insufficient knowledge of crop attributes typically results in less accurate forecasts and makes them unsuitable for agricultural precision. The suggested method combines deep learning, spectral unmixing, and hyperspectral imaging methods to overcome these obstacles. With the use of hyperspectral imaging, which records a vast array of data that is not visible to the human eye, crop attributes may be thoroughly examined and can identify the unique spectral fingerprints of different agricultural constituents by using spectral unmixing approaches, which makes it easier to evaluate the health and growth phases of the crop. Then, using this augmented spectral data, deep learning algorithms create a solid, data-driven basis for precise crop production prediction. MATLAB has been used in the suggested workflow. The combination of deep learning, spectrum unmixing, and hyperspectral imaging provides a comprehensive, cutting-edge approach that goes beyond the constraints of conventional techniques were implemented in python. Some of the algorithms that were examined, this one with integration has the lowest Root Mean Square Error (RMSE) of 0.15 and Mean Absolute Error (MAE) of 0.14, demonstrating higher prediction accuracy above other current models. This novel method represents a substantial breakthrough in precision agriculture while also improving crop production prediction.

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Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility

Cited by 2 publications

References 13 publications

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning

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