A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit <a rel="external noopener" href="https://res.mdpi.com/d_attachment/agronomy/agronomy-11-00646/article_deploy/agronomy-11-00646-v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review
<span title="2021-03-27">2021</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yws6xlpwzjf7llvhkn3wayuvfu" style="color: black;">Agronomy</a>
</i>
Preserved Fulltext
Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/agronomy11040646">doi:10.3390/agronomy11040646</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n3ru7ggspvgixlcu24meshbax4">fatcat:n3ru7ggspvgixlcu24meshbax4</a>
</span>
more »
... ng. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717173635/https://res.mdpi.com/d_attachment/agronomy/agronomy-11-00646/article_deploy/agronomy-11-00646-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/e6/13/e613959777ea5346b62e1a03a5d65a5a5c74fecd.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/agronomy11040646">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="unlock alternate icon" style="background-color: #fb971f;"></i>
mdpi.com
</button>
</a>