With the rise in global demand for staple crop products projected to substantially increase by 2050 due to population growth, rising per capita income, and the growing use of biofuels, it is necessary to adopt sustainable agricultural intensification practices in existing croplands to meet this demand. However, estimation processes currently employed in the global South remain inadequate. Traditional methods like self-reporting and crop cutting have their limitations, and remote sensing technologies are not fully utilized in this context.
and machine learning, particularly deep learning with convolutional neural networks , offer promising solutions here. To explore the scope of this new technology, researchers from Japan conducted a study focusing on rice. They used ground-based digital images taken at harvesting stage of the crop, combined with CNNs, to estimate rice yield. Their study appeared in"We started by conducting an extensive field campaign.
The insights gained from this method allowed the researchers to understand how the CNN model interpreted various features in the rice canopy images, influencing its accuracy and its ability to distinguish between yield-contributing components and non-contributing elements in the canopy. Nevertheless, the model proved robust, showing good accuracy at different shooting angles and times of day."Overall, the developed CNN model demonstrated promising capabilities in estimating rough grain yield from rice canopy images across diverse environments and cultivars. Another appealing aspect is that it is highly cost effective and does not require labor-intensive crop cuts or complex remote-sensing technologies," says Dr. Tanaka.
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