Agriculture remains an important sector of the economy. Plant diseases and pests have a big impact on plant yield and quality. So, prevention and early detection of crop disease are some of the measures that must be implemented in farming to save the plants at an early stage and thereby reduce the overall food loss. Grapes are the most profitable fruit, but they are also vulnerable to a variety of diseases. Black Measles, Black Rot, and Leaf Blight are diseases that affect grape plants. Manual disease diagnosis can result in improper identification and use of pesticides, and it takes a long time. A variety of deep learning approaches have been used to address this issue of the identification and classification of grape leaf diseases. However, there are also limits to such approaches. Therefore, this paper uses deep learning with the concept of ensemble learning based on three famous Convolutional Neural Network (CNN) architectures (Visual Geometry Group (VGG16), VGG19, and Extreme Inception (Xception)). These three models are pre-trained with ImageNet. The performance of the proposed approach is analyzed using the Plant Village (PV) dataset of common grape leaf diseases. The Proposed model gives higher performance than the results achieved by using each Deep Learning architecture separately and compared with the recent approaches in this study. The proposed system outperformed the others with 99.82% accuracy.

Instance segmentation in heterogeneous waste scenes remains challenging due to object variability, deformable shapes, partial occlusion, and large appearance differences across packaging types. This study presents a YOLOv11-based deep learning framework for mixed plastic waste instance segmentation, developed to connect visual perception with reliable material quantification. The framework integrates curated instance-level annotations, strict split isolation, multi-stage optimization, training strategy ablation, and seed-robustness analysis to support reproducible model selection. Experimental results on a held-out test set show that the optimized model achieves a mask mAP@50:95 of 0.9337, indicating strong segmentation performance under heterogeneous waste-scene conditions. To extend the analysis beyond standard vision metrics, the framework incorporates a physics-informed mask-to-mass module that converts predicted masks into class-specific mass estimates using geometric calibration and material priors. Applied to a representative stream of 1253 detected objects, the system estimated a total plastic mass of 15.48 ± 1.08 kg, corresponding to a theoretical H₂ potential of 0.41 ± 0.04 kg and a greenhouse-gas avoidance of 34.57 ± 4.15 kg CO₂e. Overall, the proposed framework extends waste-scene understanding beyond vision-level assessment toward physically grounded, data-driven decision support for smart material recovery systems.

Crops can be affected by various types of pathogens that cause diseases, leading to significant damage and negative impacts on food production and quality, causing farmers financial losses. Therefore, it is essential to detect and prevent plant diseases at their initial stages promptly. Unfortunately, the process of detecting and controlling plant diseases can be challenging for farmers. However, deep learning techniques can potentially make a significant contribution by accurately classifying plant diseases at their earliest stages. To overcome the limitations of previous research, this work proposes a new method for diagnosing and classifying plant leaf diseases. The proposed approach enhances the classification of popular crops such as tomato, potato, and pepper, using transfer ensemble learning with MobilenetV3 small and Resnet50. The model has only 37 million training parameters, which saves time, and computational power and reduces overfitting while achieving an accuracy of 99.50%. The model's ability to address false negatives and false positives ensures reliable and accurate plant disease classification at an early stage. The proposed method can work with any image dataset and achieve high performance, and it has been validated on the benchmark open dataset Plantvillage, demonstrating excellent performance for the proposed diagnosis approach.

Accurate instance segmentation of recyclable waste is important for automated sorting and circular economy applications. Mixed metal and glass municipal waste is challenging because metal surfaces are often reflective, while glass objects have transparent boundaries and high visual variability. This paper presents a controlled computer vision framework that uses instance segmentation as an object-level perception layer for mixed metal and glass waste and links eligible detections to screening-level WARM-based environmental interpretation. A seven-class annotated dataset of 1667 images was evaluated using a fixed train/validation/test split. Model development used the validation split for architecture screening, optimization tuning, transfer learning analysis, seed selection, and NMS selection, and the held-out test set was used only for final evaluation. Using mask mAP@50:95 as the primary segmentation metric, the selected YOLOv8m-seg model achieved 0.9435 on the locked test set and obtained a higher mask mAP@50:95 than Mask R-CNN and RF-DETR-Seg under the same locked test protocol. Image-level bootstrap 95% confidence intervals were also used to characterize uncertainty around the locked test segmentation comparison. For environmental reporting, mass-bearing detections were mapped to class-specific mass priors and EPA WARM v16 factors rather than direct mask area-to-mass conversion; the masks were used for instance-level separation, localization, and visual verification, not as direct physical mass measurements. Under the stated assumptions, the framework produced screening-level environmental estimates of 1113.57 MJ of energy savings and 70.20 kg CO₂e of avoided emissions, decreasing to 445.67 MJ and 28.62 kg CO₂e after applying recycling rate priors. Overall, the framework provides a leakage-aware workflow linking instance segmentation to material-specific WARM-based environmental screening.