Perception of Groundnuts Leaf Disease by Neural Network with Progressive Re-Sizing

India is the world's second-largest groundnut producer after Brazil. An major crop of oilseeds is groundnuts. Because of this, the crop's quality and yield have declined, which has had a detrimental effect on the agricultural economy. This is partly because the crop is more susceptible to various diseases. It is required to create more precise and reliable automated approaches to address this problem and improve the identification of groundnut leaf diseases. This article proposes a deep learning-driven approach based on a progressive scaling technique for the accurate classification and identification of groundnut leaf diseases. The five main groundnut leaf diseases that are the subject of this study are leaf spot, armyworm effect, wilts, yellow leaf, and healthy leaf. The proposed model is trained using both progressive resizing and conventional techniques, and its performance is assessed using cross-entropy loss. A fresh dataset is meticulously curated in Gujarat state, India's Saurashtra region, for training and validation. Due to the dataset's uneven sample distribution across disease categories, an extended focus loss function was used to correct this class imbalance. In order to evaluate the performance of the suggested model, a number of performance metrics are utilized, including accuracy, sensitivity, F1-score, precision, and sensitivity. Notably, the suggested model has a 96.12% success rate, which signifies a considerable increase in the disease identification accuracy. It's important to note that the model incorporating progressive resizing beats the basic neural network-based model based on cross-entropy loss, highlighting the potency of the recommended approach

ADAPTIVE ROUTING BASED ON DELAY TRUSTED ROUTING IN ADHOC NETWORK

Existing network hardware is constantly being improved and new communication technology continues to be developed. Together with the trend that computing hardware becomes smaller and portable, this network technology progress has led to dynamic networks. Next generation wireless networks are characterized as heterogeneous networks, particularly in terms of its underlying technology. One of the challenges of these heterogeneous networks is to manage handoff. Mobile IP is chosen for managing the handoff to accommodate the all-IP vision of the future interconnected networks. However, the handoff management of the mobile IP is mainly for data services where delay is not of a major concern. Therefore, it would be considerable challenge to achieve low latency handoff for real-time services. In this paper, we propose a multicasting scheme for delay-sensitive applications

Analysis of the Leaf Fractal Dimension

The present study deals with the analysis of leaf shapes in terms of fractal geometry with medicinal plant like Hibiscus Leaf, by using the techniques of Image Processing. Fractal analysis has been applied to describe various aspects connected with the complexity of plant morphology. In this work we determined the fractal dimension of leaves for four methods like Prewitt , Sobel , Roberts , Canny . We summarize the different methods that have been developed for estimating the fractal dimension of medicinal leaves. The results are very informative

Analysis of the Shape and Fractal Dimension of Medicinal Leaves by using Image Processing Techniques

Fractal analysis has been applied to describe various aspects connected with the complexity of plant morphology. In this work we have determined the fractal dimension of leaves. Some plants are considered as important source of nutrition and as a result, these plants are recommended for their therapeutic values. The common characteristic of such fractal objects is that their length depends on the length scale used to measure it, and the fractal dimension tells us the precise nature of this dependence. We estimated Fractal Dimension of different kinds of leaves by looking at their inner structure until to the cellular nucleus

INFLUENCES OF SODIUM ARSENITE ON SOME BLOOD PARAMETERS IN Cyprinus Carpio EXPOSED TO ARSENIC

Abstract The present study was aimed to investigate the effects of sodium arsenite on biochemical and hematologic parameters in carp (Cyprinus carpio, Linnaeus 1758) after exposed to arsenic. In this study fish were exposed to 0.01 mg/L arsenic. Our results indicated that significant suppression in Granulocyte, erythrocyte, hemoglobin, hematocrit values were decreased due to oxidative toxicity of arsenic in experimental group while comparing with control group. In addition levels of leucocyte, agranulocyte, MCV (mean corpuscular volume), MCH (mean corpuscular hemoglobin) and MCHC (mean corpuscular hemoglobin concentration) increased in the arsenic group (P<0.05) and hematological functions of common carp blood, after being exposed to arsenic

Study of Robust Watermarking Techniques with Embedding Effect

ABSTRACTS The goal of these attacks is to expose the existence of a secret message or to render a digital watermark unusable. Tradeoffs in perceptibility, bandwidth, and survivability of hidden information are also investigated

Multi-agent based context aware information gathering for agriculture using Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSN) can be used in a wide range of applications such as monitoring agriculture, infrastructures, military operations, disaster management and so on. Energy conservation is a major concern in WMSN applications. This paper proposes a multi-agent based context-aware information gathering using WMSN for monitoring agriculture. Three kinds of contexts are considered in this paper such as detecting an emergency, temporal and computational contexts for detection of diseased plants, weeds, fire and interpret the soil fertility based on the soil parameters. This work considers contexts driven by a sensor node. Whenever the context is detected the information will be sent to the sink node. The proposed scheme works as follows: Every sensor senses the information and updates the node knowledge base. Based on the sensed information node interprets the context such as disease affected plants, soil fertility, fire, and growth of weeds. The sensor nodes begin to transmit the stored information to the cluster heads with the help of Path Finding Agent (PFA). Cluster heads aggregate the information received by the sensor nodes in the field before sending this information with the help of Querry Agent (QA) to the sink node. At the sink node all the information will be sent to the end-user, but in case of the fire detection, the immediate action will be taken by the sink node itself to turn on the sprinklers. Once the sensor finishes the assigned task (sensing, communicating) then automatically it goes into sleep mode. To detect plant disease and weeds, content-based image retrieval is used to compare with the healthy or useful plant images respectively. For performance analysis, the proposed scheme is simulated using NS2. Some of the performance parameters considered in this work are context detection time, delay, fusion time and energy consumption. Keywords: Wireless multimedia sensor networks, Context-aware computing, Agent technology, Content-based image retrieva