Plants’ diseases cannot be avoided because of unpredictable climate patterns and environmental changes. The plants like ginger get affected by various pests, conditions, and nutritional deficiencies. Therefore, it is essential to identify such causes early and perform the cure to get the desired production rate. Deep learning-based methods are helpful for the identification and classification of problems in this domain. This paper presents deep artificial neural network and deep learning-based methods for the early detection of diseases, pest patterns, and nutritional deficiencies. We have used a real-field dataset consisting of healthy and affected ginger plant leaves. The results show that the convolutional neural network (CNN) has achieved the highest accuracy of 99% for disease rhizomes detection. For pest pattern leaves, VGG-16 models showed the highest accuracy of 96%. For nutritional deficiency-affected leaves, ANN has achieved the highest accuracy (96%). The experimental results achieved are comparable with other existing techniques in the literature. In addition, the results demonstrated the potential in improving the yield of ginger using the proposed disease detection methods and an essential consideration for the design of real-time disease detection applications. However, the results are specific to the dataset used in this work and may yield different results for the other datasets.
Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.
Background: The strength of one's grip is a good determining factor of their overall muscularity. Grip strength can be utilized in a variety of circumstances to assess and monitor a variety of health-related conditions. Objective: To evaluate the association of hand grip strength with BMI in cardiovascular disease patients. Methodology: An analytical cross sectional study was done on a population basis. A total of 256 patients of cardiovascular diseases were included in this study. Data were collected through purposive sampling technique. A proforma used to get demographic data including name, age, height, weight and occupation. Their BMI was also calculated. Hand grip strength was measured through handheld dynamometer of both hands. Three trials were performed by the patients from both hands. Results: Out of 256 total participants, 133 (52%) were males and 123 (48%) were females. Their mean age was 51.12 (SD = 12.513) and mean BMI was 26.533 (SD = 5.1012). Hand grip strength (HGS) and body mass index (BMI) had a negative and statistically significant association, for dominant hand (pearson’s r = -0.309, p = 0) and for non-dominant hand (pearson’s r = -0.308, p = 0). Mean hand grip strength of dominant hand was 24.677 (SD = 9.0567) and of non-dminant hand was 21.861 (SD = 8.8035). Age and duration of diseases also had a negative and statistically significant association with the hand grip strength of both hands of the patients with p value 0. Conclusion: It was concluded that the BMI had a great impact on HGS. Presence of CVD and the duration of this disease also affect HGS. With increasing age and obesity hand grip strength decreases. Keywords: Body mass index, Hand grip strength, Cardiovascular diseases.
The agriculture sector plays a crucial role in supplying nutritious and high-quality food. Plant disorders significantly impact crop productivity, resulting in an annual loss of 33%. The early and accurate detection of plant disorders is a difficult task for farmers and requires specialized knowledge, significant effort, and labor. In this context, smart devices and advanced artificial intelligence techniques have significant potential to pave the way toward sustainable and smart agriculture. This paper presents a deep learning-based android system that can diagnose ginger plant disorders such as soft rot disease, pest patterns, and nutritional deficiencies. To achieve this, state-of-the-art deep learning models were trained on a real dataset of 4,394 ginger leaf images with diverse backgrounds. The trained models were then integrated into an Android-based mobile application that takes ginger leaf images as input and performs the real-time detection of crop disorders. The proposed system shows promising results in terms of accuracy, precision, recall, confusion matrices, computational cost, Matthews correlation coefficient (MCC), mAP, and F1-score.
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