Counting Rice Grains with AI: Fast & Accurate! #Sciencefather #researchawards #artificialintelligence

 

Rice (Oryza sativa) is a key global staple, accounting for around 25% of total grain production with about 800 million tons harvested yearly. As cultivated land declines, it's essential to develop high-yielding rice varieties. One critical factor in determining yield is the number of grains per panicle. Traditionally, measuring this involves labor-intensive steps like manual threshing and counting, which are time-consuming and inefficient. Moreover, due to grain occlusion—where grains overlap each other—existing image-based methods struggle to maintain both speed and accuracy in grain counting.

Advancements in deep learning have shown great promise for automating crop analysis. Object detection algorithms like Faster R-CNN and YOLO have been successfully applied to count seeds and grains in crops like wheat and rice. For example, researchers achieved over 99% accuracy in counting threshed rice grains by combining feature pyramid networks with convolutional neural networks. However, these methods often depend on manual threshing, which is not ideal for large-scale or real-time applications. Detecting grains in their natural form—still attached and possibly overlapping—remains a major challenge.

Direct counting of rice grains in their natural form is difficult due to dense distribution, overlapping grains, and differences in shape and color across varieties. Current approaches that rely on deep learning sometimes require threshing or image preprocessing to overcome occlusion. To improve accuracy and reduce labor, researchers have begun integrating multiple deep learning models—such as object detection, image classification, and segmentation networks. For instance, combining classification models to first identify panicle morphology before detection has shown promise in enhancing accuracy. There is an urgent need for a method that quickly and accurately counts rice grains in natural conditions with minimal manual effort.

 

International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.

Visit Our Website : computer.scifat.com 

Nominate now : https://computer-vision-conferences.scifat.com/award-nomination/?ecategory=Awards&rcategory=Awardee 

Contact us : computersupport@scifat.com 

#researchawards #shorts #technology #researchers #conference #awards #professors #teachers #lecturers #biologybiologiest #OpenCV #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #DataScience #physicist #coordinator #business #genetics #medicirne #bestreseracher #bestpape 

Get Connected Here: 

================== 

Twitter :   x.com/sarkar23498

Youtube : youtube.com/channel/UCUytaCzHX00QdGbrFvHv8zA

Pinterest : pinterest.com/computervision69/

Instagram : instagram.com/saisha.leo/?next=%2F

Tumblr : tumblr.com/blog/computer-vision-research