Deep Learning for Accelerating Crop Breeding

One of the most effective strategies for increasing crop yields and improving quality is crop breeding. The breeding procedure encounters various obstacles, such as intricate data complexities, challenges in data collection, and low accuracy in predictions, which collectively hinder breeding efficiency and extend the duration of the process. Deep learning has been a revolutionary tool in crop breeding in recent years, offering the unmatched potential to accelerate the development of robust, sustainable, and high-yielding crop types. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) can quickly and accurately assess large datasets, such as hyperspectral images, genetic sequences, and environmental data. This approach facilitates the development of more productive, adaptable, and disease-resistant cultivars for agricultural use, while also accelerating crop improvement and increasing breeding efficiency. It also makes genome-wide association studies (GWAS) more successful. Global studies using deep learning approaches illustrate the contrast between traditional breeding and transformative breeding, as well as their future agricultural challenges.