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.

Differential bacterial endophytome in Foc-resistant banana cultivar displays enhanced antagonistic activity against Fusarium oxysporum f.sp. cubense (Foc).

Diverse endophytes with multiple functions exist in different banana cultivars. However, the diversity of cultivable bacterial endophytome that contributes to antifungal activity against Fusarium oxysporum f.sp. cubense (Foc) in resistant and susceptible banana cultivars is mostly unknown. In the present study, we isolated bacterial endophytes from resistant Yengambi KM5 (AAA) and susceptible banana cultivar Ney Poovan (AB) to determine the diversity of cultivable bacterial endophytes. Our study revealed the presence of 56 cultivable bacterial endophytes and 6 nectar-associated bacteria in YKM5 and 31 cultivable bacterial endophytes in Ney Poovan. The identified cultivable bacterial genera in YKM5 included Alcaligenes, Arthrobacter, Azotobacter, Acinetobacter, Agrobacterium, Bacillus, Brucella, Brevundimonas, Brachybacterium, Beijerinckia, Klebsiella, Leclercia, Lysinibacillus, Myroides, Ochrobactrum, Pseudomonas, Rhizobium, Stenotrophomonas, Serratia, and Verticiella. In Ney Poovan, the cultivable endophytic bacterial genera present were Agrobacterium, Bacillus, Bradyrhizobium, Enterobacter, Klebsiella, Lysinibacillus, Micrococcus, Ochrobactrum, Pseudomonas, Rhizobium, and Sphingobium. Thus, the composition and diversity of cultivable endophytic bacterial genera were higher in Foc-resistant YKM5. The antifungal efficacy of bacterial endophytes Brachybacterium paraconglomeratum YEBPT2 (65.5%), Brucella melitensis YEBPS3 (63.3%), Bacillus velezensis YEBBR6 (63.3%), and nectar-associated Bacillus albus YEBN2 (61.1%) from YKM5 showed the highest antifungal activity against Foc, compared with the antifungal activity of endophytes from the susceptible cultivar.