Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

TA Shaikh, T Rasool, FR Lone - Computers and Electronics in Agriculture, 2022 - Elsevier
The digitalization of data has resulted in a data tsunami in practically every industry of data-
driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has …

[HTML][HTML] The digitization of agricultural industry–a systematic literature review on agriculture 4.0

R Abbasi, P Martinez, R Ahmad - Smart Agricultural Technology, 2022 - Elsevier
Agriculture is considered one of the most important sectors that play a strategic role in
ensuring food security. However, with the increasing world's population, agri-food demands …

Machine learning in agriculture: A comprehensive updated review

L Benos, AC Tagarakis, G Dolias, R Berruto, D Kateris… - Sensors, 2021 - mdpi.com
The digital transformation of agriculture has evolved various aspects of management into
artificial intelligent systems for the sake of making value from the ever-increasing data …

A survey of deep learning techniques for weed detection from images

ASMM Hasan, F Sohel, D Diepeveen, H Laga… - … and electronics in …, 2021 - Elsevier
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection,
localisation, and recognition of objects from images or videos. DL techniques are now being …

Automation in agriculture by machine and deep learning techniques: A review of recent developments

MH Saleem, J Potgieter, KM Arif - Precision Agriculture, 2021 - Springer
Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …

[HTML][HTML] Review of weed detection methods based on computer vision

Z Wu, Y Chen, B Zhao, X Kang, Y Ding - Sensors, 2021 - mdpi.com
Weeds are one of the most important factors affecting agricultural production. The waste and
pollution of farmland ecological environment caused by full-coverage chemical herbicide …

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

R Sharma, SS Kamble, A Gunasekaran… - Computers & Operations …, 2020 - Elsevier
Agriculture plays an important role in sustaining all human activities. Major challenges such
as overpopulation, competition for resources poses a threat to the food security of the planet …

Machine learning for smart agriculture and precision farming: towards making the fields talk

TA Shaikh, WA Mir, T Rasool, S Sofi - Archives of Computational Methods …, 2022 - Springer
In almost every sector, data-driven business, the digitization of the data has generated a
data tsunami. In addition, man-to-machine digital data handling has magnified the …

A review on weed detection using ground-based machine vision and image processing techniques

A Wang, W Zhang, X Wei - Computers and electronics in agriculture, 2019 - Elsevier
Weeds are among the major factors that could harm crop yield. With the advances in
electronic and information technologies, machine vision combined with image processing …

Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion

J Chen, H Wang, H Zhang, T Luo, D Wei, T Long… - … and Electronics in …, 2022 - Elsevier
Weeds have a significant impact on sesame throughout its early stages of development, thus
they must be rigorously controlled. However, the shape of sesame seedlings and weeds are …