Narrow Results

Agriculture is India’s most common job, yet it lacks innovation and technology. As the world’s population expands, so does the demand for more food. Pesticides are used on farms to boost yield. The toxicity of the fertilizer has serious health repercussions for the farmer. So, it’s recommended to measure the amount of pesticide used and only apply it when necessary. We devised an insect-finding and insecticide-spraying mechanism. This is accomplished by employing a drone or Uninterrupted Ariel Vehicle. The drone has a camera that can photograph fields and lift pesticides weighing 3 to 4kg. After locating the insect, the insecticide is sprayed through the nozzles. In the proposed model, the Deep Convolutional Neural Network (CNN) has reached state of the art in image processing and object detection issues. Deep CNN has the potential to self-learn hidden features that help with insect detection. When compared to other similar approaches, experimental findings on a real dataset to illustrate the usefulness of the suggested methodology. We identified insects on the crop with 90% accuracy using deep CNN. It helps farmers to increase crop yield while also shielding them from the detrimental effects of spraying pesticides on the field manually.

The early detection and accurate rate of classification of plant leaf disease are more important for reliable and good agriculture, which prevents unwanted financial loss and resources. In this study, Tangent Hunter Prey Optimization-based LeNet (THPO-LeNet) is utilized for crop leaf classification and multi-class plant leaf disease identification. In this case, the input image that goes through the image pre-processing step is obtained from the plant village dataset. An adaptive Wiener filter is applied at the pre-processing stage to reduce needless mistakes and improve image quality. The pre-processed image is then sent to the leaf segmentation step, where a Mask Region-based Convolution Neural Network (Mask R-CNN) performs the segmentation. Consequently, image augmentation is performed using techniques such as scaling, rotation, translation, flipping, contrast, saturation, and hue. Afterwards, first-level classification plant leaf classification is processed by LeNet, which is optimized by Tangent Hunter Prey Optimization (THPO). The THPO is the incorporation of a Tangent Search Algorithm (TSA) and Hunter–Prey Optimizer (HPO). At last, the second-level classification of plant leaf disease is conducted by THPO-LeNet. Furthermore, the efficiency of THPO-LeNet is examined based on measures, like accuracy, Negative Predictive Value (NPV), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), loss value, and False Positive Rate (FPR), and the value attained is 95.68%, 92.50%, 91.48%, 92.25%, 92.54%, 8.321%, and 7.754%, respectively.

Ecologists have been studying different computational models in the classification of ecological species. In this paper, we intend to take advantages of variant deep-learning models, including LeNet, AlexNet, VGG models, residual neural network, and inception models, to classify ecological datasets, such as bee wing and butterfly. Since the datasets contain relatively small data samples and unbalanced samples in each class, we apply data augmentation and transfer learning techniques. Furthermore, newly designed inception residual and inception modules are developed to enhance feature extraction and increase classification rates. As comparing against currently available deep-learning models, experimental results show that the proposed inception residual block can avoid the vanishing gradient problem and achieve a high accuracy rate of 92%.

Bones undergo significant changes in size and shape with the growth of the child, and bone age estimation is crucial for determining the growth, genetic and endocrine disorders in children. Hand X-ray images are extensively utilized for diagnosing disorders in children. The variation in the chronological age and bone age indicates the presence of endocrine disorders, genetic problems, and growth abnormalities. Traditionally, bone age is estimated manually by inspecting the X-ray images, which is extremely time-consuming and prone to error. Further, the accuracy of the bone estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, to overcome these issues, it is essential to devise automatic methods that can estimate the bone with high accuracy and in a short duration. In this work, bone age is estimated using a Deep Residual Network (DRN), whose learnable factors are adjusted using the devised Beluga Whale Lion Optimization (BWLO) algorithm. Further, the BWLO_DRN is examined for its superiority considering metrics, like accuracy, True Positive Rate (TPR), and True Negative Rate (TNR), and the corresponding values of 89.8%, 86.8%, and 90% are found to be achieved from the experimental results.