  Development of either drought-resistant or drought-tolerant varieties in rice
(Oryza sativa L.), especially for high yield in the context of climate change,
is a crucial task across the world. The need for high yielding rice varieties
is a prime concern for developing nations like India, China, and other
Asian-African countries where rice is a primary staple food. The present
investigation is carried out for discriminating drought tolerant, and
susceptible genotypes. A total of 150 genotypes were grown under controlled
conditions to evaluate at High Throughput Plant Phenomics facility, Nanaji
Deshmukh Plant Phenomics Centre, Indian Council of Agricultural Research-Indian
Agricultural Research Institute, New Delhi. A subset of 10 genotypes is taken
out of 150 for the current investigation. To discriminate against the
genotypes, we considered features such as the number of leaves per plant, the
convex hull and convex hull area of a plant-convex hull formed by joining the
tips of the leaves, the number of leaves per unit convex hull of a plant,
canopy spread - vertical spread, and horizontal spread of a plant. We trained
You Only Look Once (YOLO) deep learning algorithm for leaves tips detection and
to estimate the number of leaves in a rice plant. With this proposed framework,
we screened the genotypes based on selected traits. These genotypes were
further grouped among different groupings of drought-tolerant and drought
susceptible genotypes using the Ward method of clustering.
