| Summary: | <ul>
<li>There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing their number and size is a potential route to elevate photosynthetic activity. Notably, bundle sheath cells do not make a significant contribution to overall carbon fixation in rice, and thus, various attempts are being made to increase chloroplast content specifically in this cell type.</li>
<li>In this study, we developed and applied a deep learning tool, Chloro-Count, and used it to quantify chloroplast dimensions in bundle sheath cells of <em>OsHAP3H</em> gain- and loss-of-function mutants in rice.</li>
<li>Loss of <em>OsHAP3H</em> increased chloroplast occupancy in bundle sheath cells by 50%. When grown in the field, mutants exhibited increased numbers of tillers and panicles. The implementation of Chloro-Count enabled precise quantification of chloroplasts in loss- and gain-of-function <em>OsHAP3H</em> mutants and facilitated a comparison between 2D and 3D quantification methods.</li>
<li>Collectively, our observations revealed that a mechanism operates in bundle sheath cells to restrict chloroplast occupancy as cell dimensions increase. That mechanism is unperturbed in <em>Oshap3H</em> mutants but loss of <em>OsHAP3H</em> function leads to an increase in chloroplast numbers. The use of Chloro-Count also revealed that 2D quantification is compromised by the positioning of chloroplasts within the cell.</li>
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