AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering.

Background: Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders.

Results: Advanced deep learning (DL) techniques such as Segment Anything Model (SAM) and YOLO foundation models (YOLOv7, YOLOv8, YOLOv9, and YOLO-NAS), were used to accurately categorize PPD severity from RGB images captured by cameras or cell phones.

Participatory AI for inclusive crop improvement.

Context: Crop breeding in the Global South faces a 'phenotyping bottleneck' due to reliance on manual visual phenotyping, which is both error-prone and challenging to scale across multiple environments, inhibiting selection of germplasm adapted to farmer production environments. This limitation impedes rapid varietal turnover, crucial for maintaining high yields and food security under climate change. Low adoption of improved varieties results from a top-down system in which farmers have been more passive recipients than active participants in varietal development.

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI.

Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH emissions from rice fields is crucial for understanding gas concentrations.

In pursuit of a better world: crop improvement and the CGIAR.

The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers.

Field-transcriptome analyses reveal developmental transitions during flowering in cassava (Manihot esculenta Crantz).

We characterized genes that function in the photoperiodic flowering pathway in cassava. Transcriptome analysis of field-grown plants revealed characteristic expression patterns of these genes, demonstrating that field-grown cassava experiences two distinct developmental transitions. Cassava is an important crop for both edible and industrial purposes.

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava ( Crantz).

Background: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data.

Expression of the CCCH-tandem zinc finger protein gene OsTZF5 under a stress-inducible promoter mitigates the effect of drought stress on rice grain yield under field conditions.

Increasing drought resistance without sacrificing grain yield remains an ongoing challenge in crop improvement. In this study, we report that Oryza sativa CCCH-tandem zinc finger protein 5 (OsTZF5) can confer drought resistance and increase grain yield in transgenic rice plants. Expression of OsTZF5 was induced by abscisic acid, dehydration and cold stress.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images.

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images.

A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava ().

Background: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent regulation of storage root (SR) differentiation, due in part to the innate problems of studying storage roots and the lack of a suitable model system for monitoring storage root growth. The research presented here aimed to develop a reliable, low-cost effective system that enables the study of the factors influencing cassava storage root initiation and development.

Translocation of a parthenogenesis gene candidate to an alternate carrier chromosome in apomictic Brachiaria humidicola.

Background: The apomictic reproductive mode of Brachiaria (syn. Urochloa) forage species allows breeders to faithfully propagate heterozygous genotypes through seed over multiple generations. In Brachiaria, reproductive mode segregates as single dominant locus, the apospory-specific genomic region (ASGR).

Drought Response in Wheat: Key Genes and Regulatory Mechanisms Controlling Root System Architecture and Transpiration Efficiency.

Abiotic stresses such as, drought, heat, salinity, and flooding threaten global food security. Crop genetic improvement with increased resilience to abiotic stresses is a critical component of crop breeding strategies. Wheat is an important cereal crop and a staple food source globally.

Ground penetrating radar: a case study for estimating root bulking rate in cassava ( Crantz).

Background: Understanding root traits is a necessary research front for selection of favorable genotypes or cultivation practices. Root and tuber crops having most of their economic potential stored below ground are favorable candidates for such studies. The ability to image and quantify subsurface root structure would allow breeders to classify root traits for rapid selection and allow agronomist the ability to derive effective cultivation practices.

Expression of the Gene Enhances Rice Yield under Field Drought at the Reproductive Stage.

We evaluated the yields of L. 'Nipponbare' rice lines expressing a gene encoding an A20/AN1 domain stress-associated protein, AlSAP, from the halophyte grass under the control of different promoters. Three independent field trials were conducted, with drought imposed at the reproductive stage.

Overexpression of an Arabidopsis thaliana galactinol synthase gene improves drought tolerance in transgenic rice and increased grain yield in the field.

Drought stress has often caused significant decreases in crop production which could be associated with global warming. Enhancing drought tolerance without a grain yield penalty has been a great challenge in crop improvement. Here, we report the Arabidopsis thaliana galactinol synthase 2 gene (AtGolS2) was able to confer drought tolerance and increase grain yield in two different rice (Oryza sativa) genotypes under dry field conditions.

Development and field performance of nitrogen use efficient rice lines for Africa.

Nitrogen (N) fertilizers are a major input cost in rice production, and its excess application leads to major environmental pollution. Development of rice varieties with improved nitrogen use efficiency (NUE) is essential for sustainable agriculture. Here, we report the results of field evaluations of marker-free transgenic NERICA4 (New Rice for Africa 4) rice lines overexpressing barley alanine amino transferase (HvAlaAT) under the control of a rice stress-inducible promoter (pOsAnt1).

OsMADS26 Negatively Regulates Resistance to Pathogens and Drought Tolerance in Rice.

Functional analyses of MADS-box transcription factors in plants have unraveled their role in major developmental programs (e.g. flowering and floral organ identity) as well as stress-related developmental processes, such as abscission, fruit ripening, and senescence.

Development of disease-resistant rice by optimized expression of WRKY45.

The rice transcription factor WRKY45 plays a central role in the salicylic acid signalling pathway and mediates chemical-induced resistance to multiple pathogens, including Magnaporthe oryzae and Xanthomonas oryzae pv. oryzae. Previously, we reported that rice transformants overexpressing WRKY45 driven by the maize ubiquitin promoter were strongly resistant to both pathogens; however, their growth and yield were negatively affected because of the trade-off between the two conflicting traits.