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

Concerns about methane (CH4) 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 CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

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. Traditional methods relying solely on farmers' ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.

Identification of QTLs for pod and kernel traits in cultivated peanut by bulked segregant analysis

Bulked segregant analysis was used to identify simple sequence repeat (SSR) markers associated with pod and kernel traits in cultivated peanut, to permit rapid selection of superior quality genotypes in the breeding program. SSR markers linked to pod and kernel traits were identified in two DNA pools (high and low), which were established using selected F2:6 recombinant individuals resulting from a cultivated cross between a runner (Tamrun OL01) and a Spanish (BSS 56) peanut. To identify quantitative trait loci (QTLs) for pod and kernel-related traits, parents were screened initially with 112 SSR primer pairs. The survey revealed 8.9 percent polymorphism between parents. Of ten SSR primer pairs distinguishing the parents, five (PM375, PM36, PM45, pPGPseq8D9, and Ah-041) were associated with differences between bulks for seed length, pod length, number of pods per plant, 100-seed weight, maturity, or oil content. Association was confirmed by analysis of segregation among 88 F2:6 individuals in the RIL population. Phenotypic means associated with markers for three traits differed by more than 40 percent, indicating the presence of QTLs with major effects for number of pods per plant, plant weight, and pod maturity. The SSR markers can be used for marker assisted selection for quality and yield improvement in peanut. To the best of our knowledge, this is the first report on the identification of SSR markers linked to pod - and kernel- related traits in cultivated peanut.

Cloning and characterization of resistance gene analogs from under-exploited plant species

Genomic DNA sequences sharing homology with NBS region of resistance gene analogs were isolated and characterized from Pongamia glabra, Adenanthera pavonina, Clitoria ternatea and Solanum trilobatum using PCR based approach with primers designed from conserved regions of NBS domain. The presence of consensus motifs viz., kinase 1a, kinase 2, kinase 3a and hydrophobic domain provided evidence that the cloned sequences may belong to the NBS-LRR gene family. Conservation of tryptophan as the last residue of kinase-2 motif further confirms their position in non-TIR NBS-LRR family of resistance genes. The Resistance Gene Analogs (RGAs) cloned from P. glabra, A. pavonina, C. ternatea and S. trilobatum clustered together with well- characterized non-TIR-NBS-LRR genes leaving the TIR-NBS-LRR genes as a separate cluster in the average distance tree constructed based on BLOSUM62. All the four RGAs had high level of identity with NBS-LRR family of RGAs deposited in the GenBank. The extent of identity between the sequences at NBS region varied from 29 percent (P. glabra and S. trilobatum) to 78 percent (A. pavonina and C. ternatea), which indicates the diversity among the RGAs.