The path to smart farming: Innovations and opportunities in precision agriculture

E Karunathilake, AT Le, S Heo, YS Chung, S Mansoor - Agriculture, 2023 - mdpi.com
Precision agriculture employs cutting-edge technologies to increase agricultural productivity
while reducing adverse impacts on the environment. Precision agriculture is a farming …

Power consumption analysis, measurement, management, and issues: A state-of-the-art review of smartphone battery and energy usage

PKD Pramanik, N Sinhababu, B Mukherjee… - ieee …, 2019 - ieeexplore.ieee.org
The advancement and popularity of smartphones have made it an essential and all-purpose
device. But lack of advancement in battery technology has held back its optimum potential …

Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk

M Ayaz, M Ammad-Uddin, Z Sharif, A Mansour… - IEEE …, 2019 - ieeexplore.ieee.org
Despite the perception people may have regarding the agricultural process, the reality is that
today's agriculture industry is data-centered, precise, and smarter than ever. The rapid …

A review on the leaf area index (LAI) in vertical greening systems

A De Bock, B Belmans, S Vanlanduit, J Blom… - Building and …, 2023 - Elsevier
The leaf area index (LAI) is a key dynamic parameter in Vertical Greening Systems (VGS). It
quantifies the total amount of leaf area in the canopy and largely determines the extent of co …

Plant disease identification using shallow convolutional neural network

SM Hassan, M Jasinski, Z Leonowicz, E Jasinska… - Agronomy, 2021 - mdpi.com
Various plant diseases are major threats to agriculture. For timely control of different plant
diseases in effective manner, automated identification of diseases are highly beneficial. So …

Trends and prospect of machine vision technology for stresses and diseases detection in precision agriculture

J Shin, MS Mahmud, TU Rehman, P Ravichandran… - AgriEngineering, 2022 - mdpi.com
Introducing machine vision-based automation to the agricultural sector is essential to meet
the food demand of a rapidly growing population. Furthermore, extensive labor and time are …

Proximal methods for plant stress detection using optical sensors and machine learning

AV Zubler, JY Yoon - Biosensors, 2020 - mdpi.com
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the
visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) …

Smartphone-based detection of leaf color levels in rice plants

M Tao, X Ma, X Huang, C Liu, R Deng, K Liang… - … and electronics in …, 2020 - Elsevier
Leaf color is correlated with nitrogen content, and detection of nitrogen content in rice leaves
is important for guiding farmers in applying fertilizer. However, the performance of existing …

Computer vision-based platform for apple leaves segmentation in field conditions to support digital phenotyping

A Uryasheva, A Kalashnikova, D Shadrin… - … and Electronics in …, 2022 - Elsevier
Computer vision and machine learning have recently been applied to a number of sensing
platforms, boosting their performance to a new level. These advances have shown the vast …

Integrating non-invasive VIS-NIR and bioimpedance spectroscopies for stress classification of sweet basil (Ocimum basilicum L.) with machine learning

D Son, J Park, S Lee, JJ Kim, S Chung - Biosensors and Bioelectronics, 2024 - Elsevier
Plant stress diagnosis is essential for efficient crop management and productivity increase.
Under stress, plants undergo physiological and compositional changes. Vegetation indices …