Fuzzy Logic Controlled Motor Speed in Rotating Aquaponics Based on Lactuca sativa Leaf Chlorosis and Necrosis and Environment Temperature

Aquino, Heinrick; Concepcion, Ronnie; Mendigoria, Christan Hail; Alajas, Oliver John; Dadios, Elmer P.; Cuello, Joel L.

doi:10.1109/hnicem54116.2021.9731945

Cited by 4 publications

(1 citation statement)

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“…For example, nutritional status detection can be performed with a VIS/SWIR sensor (visible to short wave infrared bands), and a good outcome was carried out with a Radial Basis Function Network based on an SVM [169]. The SVM classifier is an effective algorithm for detecting and classifying leaves affected by chlorosis in lettuce with 100% accuracy [170]. In-field phenotyping is often carried out with RGB images that can express great potential through pretrained CNN combined with a long-short term memory network [171].…”

Section: Plant Healthmentioning

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: Plant Healthmentioning

confidence: 99%