Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

GA Mesías-Ruiz, M Pérez-Ortiz, J Dorado… - Frontiers in Plant …, 2023 - frontiersin.org
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current
context of climate change, which is causing the destabilization of agricultural practices and …

Applied deep learning-based crop yield prediction: A systematic analysis of current developments and potential challenges

K Meghraoui, I Sebari, J Pilz, K Ait El Kadi, S Bensiali - Technologies, 2024 - mdpi.com
Agriculture is essential for global income, poverty reduction, and food security, with crop
yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant …

[HTML][HTML] Analysis of Stable Diffusion-derived fake weeds performance for training Convolutional Neural Networks

H Moreno, A Gómez, S Altares-López, A Ribeiro… - … and Electronics in …, 2023 - Elsevier
Weeds challenge crops by competing for resources and spreading diseases, impacting crop
yield and quality. Effective weed detection can enhance herbicide application, thus reducing …

[HTML][HTML] Early weed identification based on deep learning: A review

Y Zhang, M Wang, D Zhao, C Liu, Z Liu - Smart Agricultural Technology, 2023 - Elsevier
Weeds were one of the most destructive constraints on crop production and posed a
significant threat to agricultural productivity. The increasing development of smart agriculture …

Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale crop** systems

GRY Coleman, A Bender, K Hu, SM Sharpe… - Weed …, 2022 - cambridge.org
The past 50 yr of advances in weed recognition technologies have poised site-specific weed
control (SSWC) on the cusp of requisite performance for large-scale production systems …

VegAnn, vegetation annotation of multi-crop RGB images acquired under diverse conditions for segmentation

S Madec, K Irfan, K Velumani, F Baret, E David… - Scientific Data, 2023 - nature.com
Applying deep learning to images of crop** systems provides new knowledge and
insights in research and commercial applications. Semantic segmentation or pixel-wise …

When crops meet machine vision: A review and development framework for a low-cost nondestructive online monitoring technology in agricultural production

X Lv, X Zhang, H Gao, T He, Z Lv… - Agriculture …, 2024 - Elsevier
Abstract The Food and Agriculture Organization (FAO) has indicated that digital technology
is key for improving the resilience of food systems. Smart models have been developed for …

Laser weeding: opportunities and challenges for couch grass (Elymus repens (L.) Gould) control

C Andreasen, E Vlassi, N Salehan - Scientific Reports, 2024 - nature.com
Laser weeding may contribute to less dependency on herbicides and soil tillage. Several
research and commercial projects are underway to develop robots equipped with lasers to …

[HTML][HTML] Deep learning YOLO-based solution for grape bunch detection and assessment of biophysical lesions

I Pinheiro, G Moreira, D Queirós da Silva, S Magalhães… - Agronomy, 2023 - mdpi.com
The world wine sector is a multi-billion dollar industry with a wide range of economic
activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more …

[HTML][HTML] CNN-MLP-Based configurable robotic arm for Smart Agriculture

M Li, F Wu, F Wang, T Zou, M Li, X **ao - Agriculture, 2024 - mdpi.com
Amidst escalating global populations and dwindling arable lands, enhancing agricultural
productivity and sustainability is imperative. Addressing the inefficiencies of traditional …