Cassava molecular genetics and genomics for enhanced resistance to diseases and pests

Abstract

Cassava (Manihot esculenta) is one of the most important sources of dietary calories in the tropics, playing a central role in food and economic security for smallholder farmers. Cassava production is highly constrained by several pests and diseases, mostly cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). These diseases cause significant yield losses, affecting food security and the livelihoods of smallholder farmers. Developing resistant varieties is a good way of increasing cassava productivity. Although some levels of resistance have been developed for some of these diseases, there is observed breakdown in resistance for some diseases, such as CMD. A frequent re‐evaluation of existing disease resistance traits is required to make sure they are still able to withstand the pressure associated with pest and pathogen evolution. Modern breeding approaches such as genomic‐assisted selection in addition to biotechnology techniques like classical genetic engineering or genome editing can accelerate the development of pest‐ and disease‐resistant cassava varieties. This article summarizes current developments and discusses the potential of using molecular genetics and genomics to produce cassava varieties resistant to diseases and pests.

Identifying genetically redundant accessions in the world’s largest cassava collection

“…Cassava is currently the third-largest source of carbohydrates for human consumption in the world after rice and maize, playing a crucial role in ensuring food security, particularly in many low-income countries ( Howeler et al., 2013 ). Global cassava production exhibits significant yields in Africa (64.8%), Asia (26.9%), and Latin America (8.3%) ( FAOSTAT, 2021 ), with a total production of 314 million tons in 2021.While productivity is influenced by factors such as agronomic practices, climate, and pest management, major challenges include susceptibility to pests and diseases, post-harvest losses, and the pressing need for improved varieties with higher yields and nutrition content ( Montagnac et al., 2009 ; Zainuddin et al., 2018 ; Ntui et al., 2023 ).…”

“…(8.3%) (FAOSTAT, 2021), with a total production of 314 million tons in 2021.While productivity is influenced by factors such as agronomic practices, climate, and pest management, major challenges include susceptibility to pests and diseases, post-harvest losses, and the pressing need for improved varieties with higher yields and nutrition content (Montagnac et al, 2009;Zainuddin et al, 2018;Ntui et al, 2023).…”

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

“…Drones 2024, 8, x FOR PEER REVIEW 5 of 24 (4) We proposed a learning rate optimization method based on the sparrow search algorithm (SSA) to obtain the optimal learning rate for the highest average precision, thereby enhancing the model's robustness and detection accuracy; (5) With the improved YOLOv8 model, we achieved an accuracy of 97.59%, a recall rate of 94.40%, a frame rate of 40.62 FPS, model parameters of 14.62 MB, and GFLOPs of 11.21. These results demonstrate the model's capability to undertake large-scale corn tassel recognition and counting tasks with UAVs under complex conditions [22].…”

“…For example, farmers can predict yields based on the maturity of corn tassels, schedule harvest times appropriately, and avoid yield losses due to premature or delayed harvesting. Additionally, monitoring corn tassels facilitates the early detection and management of potential issues during growth stages, such as pests, diseases (Ntui et al, 2024) [4], and nutrient deficiencies, ensuring healthy growth and stable corn yields.…”

“…k f ab represents the weights corresponding to X u (i, j), where a, b ∈ X u (i, j). The convolutional path operation is detailed in Equation (4). Following this operation, the feature maps undergo shifting and aggregation processes, enabling the gathering of information from local receptive fields.…”