Occlusion Handler Density Networks for 3D Multimodal Joint Location of Hand Pose Hypothesis

Predicting the pose parameters during the hand pose estimation (HPE) process is an ill-posed challenge. This is due to severe self-occluded joints of the hand. The existing approaches for predicting pose parameters of the hand, utilize a single-value mapping of an input image to generate final pose output. This way makes it difficult to handle occlusion especially when it comes from the multimodal pose hypothesis. This paper introduces an effective method of handling multimodal joint occlusion using the negative log-likelihood of a multimodal mixture-of-Gaussians through a hybrid hierarchical mixture density network (HHMDN). The proposed approach generates multiple feasible hypotheses of 3D poses with visibility, unimodal and multimodal distribution units to locate joint visibility. The visible features are extracted and fed into the Convolutional Neural Networks (CNN) layer of the HHMDN for feature learning. Finally, the effectiveness of the proposed method is proved on ICVL, NYU, and BigHand public hand pose datasets. The imperative results show that the proposed method in this paper is effective as it achieves a visibility error of 30.3mm, which is less error compared to many state-of-the-art approaches that use different distributions of visible and occluded joints

Application of MobileNets Convolutional Neural Network Model in Detecting Tomato Late Blight Disease

Late blight (LB) disease causes significant annual losses in tomato production. Early identification of this disease is crucial in halting its severity. This study aimed to leverage the strength of Convolutional Neural Networks (CNNs) in automated prediction of tomato LB. Through transfer learning, the MobileNetV3 model was trained on high-quality, well-labeled images from Kaggle datasets. The trained model was tested on different images of healthy and infected leaves taken from different real-world locations in Mbeya, Arusha, and Morogoro. Test results demonstrated the model's success in identifying LB disease, with an accuracy of 81% and a precision of 76%. The trained model has the potential to be integrated into an offline mobile app for real-time use, improving the efficiency and effectiveness of LB disease detection in tomato production. Similar methods could also be applied to detect other tomato infections. Keywords:  MobileNets; convolutional neural networks; plant diseases detection; image classification; transfer learnin