[HTML][HTML] Automation and digitization of agriculture using artificial intelligence and internet of things
A Subeesh, CR Mehta - Artificial Intelligence in Agriculture, 2021 - Elsevier
The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …
agriculture sector to increase food-grain production and productivity. In most of the countries …
Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review
DI Patrício, R Rieder - Computers and electronics in agriculture, 2018 - Elsevier
Grain production plays an important role in the global economy. In this sense, the demand
for efficient and safe methods of food production is increasing. Information Technology is …
for efficient and safe methods of food production is increasing. Information Technology is …
Classification of rice varieties with deep learning methods
Rice, which is among the most widely produced grain products worldwide, has many genetic
varieties. These varieties are separated from each other due to some of their features. These …
varieties. These varieties are separated from each other due to some of their features. These …
Deep learning for plant stress phenoty**: trends and future perspectives
Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile
tool to assimilate large amounts of heterogeneous data and provide reliable predictions of …
tool to assimilate large amounts of heterogeneous data and provide reliable predictions of …
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems
With being rapid increasing population in worldwide, the need for satisfactory level of crop
production with decreased amount of agricultural lands. Machine vision would ensure the …
production with decreased amount of agricultural lands. Machine vision would ensure the …
Plant disease identification using explainable 3D deep learning on hyperspectral images
Background Hyperspectral imaging is emerging as a promising approach for plant disease
identification. The large and possibly redundant information contained in hyperspectral data …
identification. The large and possibly redundant information contained in hyperspectral data …
A mechanistic review on machine learning-supported detection and analysis of volatile organic compounds for food quality and safety
Y Feng, Y Wang, B Beykal, M Qiao, Z ** and machine learning for plant stress phenoty**
T Gill, SK Gill, DK Saini, Y Chopra, JP de Koff… - Phenomics, 2022 - Springer
During the last decade, there has been rapid adoption of ground and aerial platforms with
multiple sensors for phenoty** various biotic and abiotic stresses throughout the …
multiple sensors for phenoty** various biotic and abiotic stresses throughout the …
Challenges and opportunities in machine-augmented plant stress phenoty**
Plant stress phenoty** is essential to select stress-resistant varieties and develop better
stress-management strategies. Standardization of visual assessments and deployment of …
stress-management strategies. Standardization of visual assessments and deployment of …
From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy
The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting
symptoms, is a key quantitative variable to know for many diseases and is prone to error …
symptoms, is a key quantitative variable to know for many diseases and is prone to error …