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.

Effects of Oral Pirfenidone on Colon Anastomosis Healing and Adhesion Formation in Rats.

INTRODUCTION: Many experimental studies have examined multiple drugs or treatments to improve the healing of intestinal anastomoses. Synthetic prostacyclin analogs, immunosuppressants, erythropoietin, growth hormone, insulin-like growth factor type 1, synthetic metalloproteinases inhibitors, and hyperbaric oxygen therapy have produced promising results in low-risk models of anastomosis dehiscence. However, in high-risk models, only hyperbaric oxygen therapy has been shown to be useful. Pirfenidone (PFD), a commonly used antifibrosing drug, has not been shown to be effective for this purpose. Our objective was to evaluate the effects of PFD on anastomosis healing and adhesion genesis in a low-risk rat model of dehiscence of colonic anastomosis. METHODS: An experimental study was conducted on 40 healthy Wistar rats randomly assigned to the control group or PFD experimental group (20 rats in each group). Colon anastomosis was performed 3 cm above the peritoneal reflection using the same technique in all animals. Mechanical resistance was studied by measuring bursting pressure. Adhesions were evaluated macroscopic and histologically using common staining techniques. Animals received the first PFD dose 12 h after surgery at a dose of 500 mg/kg one a day (SID) for 5 consecutive days. On day 6, the animals were reoperated on to measure the bursting pressure in situ and to classify adhesions macroscopically, and the anastomosed colon was resected for histological analysis. RESULTS: There were no deaths, complications, or anastomosis dehiscence in either group. The mean bursting pressure was 120.8 ± 11 mm Hg and 135.5 ± 12.4 in the control and PFD groups, respectively (p < 0.001). The adhesions were less dense and had less inflammatory cell infiltration in the PFD group (p < 0.02 and 0.002, respectively). Collagen content was slightly higher in the PFD group (p = 0.04). CONCLUSIONS: Our results revealed favorable effects of PFD in this low-risk colon anastomosis model; for example, the bursting pressure was higher, and the macroscopic adhesions were soft and exhibited less inflammatory infiltration and higher collagen content in the PFD group than in the control group. The results showing that PFD treatment was associated with better healing of minor adhesions seem to be paradoxical because the therapeutic indications for this drug are directed at treating fibrosing diseases.

Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations.

Root rot in common bean is a disease that causes serious damage to grain production, particularly in the upland areas of Eastern and Central Africa where significant losses occur in susceptible bean varieties. Pythium spp. and Fusarium spp. are among the soil pathogens causing the disease. In this study, a panel of 228 lines, named RR for root rot disease, was developed and evaluated in the greenhouse for Pythium myriotylum and in a root rot naturally infected field trial for plant vigor, number of plants germinated, and seed weight. The results showed positive and significant correlations between greenhouse and field evaluations, as well as high heritability (0.71-0.94) of evaluated traits. In GWAS analysis no consistent significant marker trait associations for root rot disease traits were observed, indicating the absence of major resistance genes. However, genomic prediction accuracy was found to be high for Pythium, plant vigor and related traits. In addition, good predictions of field phenotypes were obtained using the greenhouse derived data as a training population and vice versa. Genomic predictions were evaluated across and within further published data sets on root rots in other panels. Pythium and Fusarium evaluations carried out in Uganda on the Andean Diversity Panel showed good predictive ability for the root rot response in the RR panel. Genomic prediction is shown to be a promising method to estimate tolerance to Pythium, Fusarium and root rot related traits, indicating a quantitative resistance mechanism. Quantitative analyses could be applied to other disease-related traits to capture more genetic diversity with genetic models.