A review of deep learning techniques used in agriculture
Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …
shown great promise in the agricultural sector. In this study, 129 papers that are based on …
Artificial intelligence, machine learning and big data in natural resources management: a comprehensive bibliometric review of literature spanning 1975–2022
Applying artificial intelligence (AI), machine learning (ML), and big data to natural resource
management (NRM) is revolutionizing how natural resources are managed. To gain more …
management (NRM) is revolutionizing how natural resources are managed. To gain more …
Tomato maturity recognition with convolutional transformers
Tomatoes are a major crop worldwide, and accurately classifying their maturity is important
for many agricultural applications, such as harvesting, grading, and quality control. In this …
for many agricultural applications, such as harvesting, grading, and quality control. In this …
YOLOv5s-CBAM-DMLHead: A lightweight identification algorithm for weedy rice (Oryza sativa f. spontanea) based on improved YOLOv5
C Yuan, T Liu, F Gao, R Zhang, X Seng - Crop Protection, 2023 - Elsevier
Rice (Oryza sativa L.) is one of the essential food sources for people, with rice farms
producing about 480 million tons of refined rice annually. In recent years, with the …
producing about 480 million tons of refined rice annually. In recent years, with the …
Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review
One of the most widely researched topics in the food industry is bread quality analysis.
Different techniques have been developed to assess the quality characteristics of bakery …
Different techniques have been developed to assess the quality characteristics of bakery …
Estimation of the extent of the vulnerability of agriculture to climate change using analytical and deep-learning methods: a case study in Jammu, Kashmir, and Ladakh
Climate stress poses a threat to the agricultural sector, which is vital for both the economy
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …
[PDF][PDF] Unmanned aerial vehicle-based applications in smart farming: A systematic review
On one hand, the emergence of cutting-edge technologies like AI, Cloud Computing, and
IoT holds immense potential in Smart Farming and Precision Agriculture. These …
IoT holds immense potential in Smart Farming and Precision Agriculture. These …
Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review
SE Teet, N Hashim - Food Control, 2023 - Elsevier
Fruits and vegetables are among the agricultural products that enjoy a high demand in the
market. However, the most critical challenge in the production of fruits and vegetables is …
market. However, the most critical challenge in the production of fruits and vegetables is …
Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment
X Wang, J Liu - Scientific Reports, 2024 - nature.com
This study introduces YOLOv8n-vegetable, a model designed to address challenges related
to imprecise detection of vegetable diseases in greenhouse plant environment using …
to imprecise detection of vegetable diseases in greenhouse plant environment using …
Challenges and practices of deep learning model reengineering: A case study on computer vision
Many engineering organizations are reimplementing and extending deep neural networks
from the research community. We describe this process as deep learning model …
from the research community. We describe this process as deep learning model …