Development of AI methods for seed yield prediction using RGB images: Progress Report - Arrechea, Darwin A.; Cardoso Arango, Juan Andrés (CGIAR Initiative on Accelerated Breeding, 2023)
In the realm of agricultural advancement, the intersection of technology and farming practices has been a cornerstone for enhancing productivity and sustainability. As global food demands continue to rise, innovative solutions are crucial for maximizing agricultural outputs. This report focuses on one such innovative frontier: the application of artificial intelligence (AI) in agriculture, specifically in predicting seed yield. The focal point of our exploration is the automated counting of seeds in forages using DL algorithms. The current landscape of agricultural technology reveals a growing need for more accurate and efficient methods of predicting seed yields. Traditional manual-labor approaches, while beneficial, often fall short in terms of precision and scalability. The advent of AI promises a transformative shift in this domain. These technologies have already shown their prowess in various sectors and are now poised to revolutionize agricultural practices. The main goal of this project is to develop and refine AI methods that leverage RGB images for predicting seed yields. The initial choice of RGB images stems from their proven effectiveness in capturing vital plant health indicators, which are essential for accurate yield predictions. By enhancing the accuracy and efficiency of seed counting, is possible to have an efficient yield prediction. This project aims to empower farmers with actionable insights, optimize resource allocation, and ultimately contribute to higher and more sustainable agricultural outputs. This report will detail the methodologies employed, the experimental setup, data analysis, and the results obtained.