Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing cro...
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ISBN:
(纸本)9789819720880
Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. This undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. The proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. The images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. The chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. These layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. The findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. The efficacious application of this model can assist farmers and agricultural specialists in inform
Vehicular networks that consist of interlinked automobiles and transportation network are prone to cyberattacks due to the increased use of software and the presence of wireless interfaces. To counter these threats, i...
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The use of the ADAM (Adaptive Moment Estimation) and SGD (Stochastic Gradient Descent) algorithms to optimize the YOLOv7(You Only Look Once), YOLOv8, and YOLO-NAS models for weed detection in agricultural landscapes i...
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作者：
Ghanem, Sahar I.
Computer Science and Artificial Intelligence Faculty Artificial Intelligence and Machine Learning Department Egypt
In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and per...
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In this modern era, the demand for efficient and automated cricket video summarization techniques is rapidly increasing. This paper introduces an innovative and advance neural network system that transforms the way cr...
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Emotional state of a person can be found out through various modalities such as using facial expressions, voice of the person, text and so on. Our study is solely based on the emotion extracted through the text. Past ...
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作者：
Nivetha, N.Usharani, S.
Department of Computer Science and Engineering Villupuram India
Department of Artificial Intelligence and Machine Learning Villupuram India
Precision agriculture has become a major change in crop farming. It utilises cutting-edge technologies to maximise field-level management. Precision agriculture has completely transformed crop production by leveraging...
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ISBN:
(纸本)9798350386578
Precision agriculture has become a major change in crop farming. It utilises cutting-edge technologies to maximise field-level management. Precision agriculture has completely transformed crop production by leveraging the latest developments to maximize field-level management. Predicting crop yields with accuracy helps farmers reduce their environmental impact, increase productivity, and make well-informed decisions. Accurate and timely insights are frequently lacking in traditional agricultural yield prediction approaches. The study offers a deep learning method for precisely predicting agricultural yields. Accurate crop yield forecasts assist farmers in minimizing their negative environmental effects, boosting productivity, and making educated choices. However, there are many obstacles because conventional agricultural yield prediction methods frequently need more timely and precise insights. Despite their success, several challenges still exist. These include handling heterogeneous data, dealing with missing values, and the complexity of capturing non-linear relationships in the data. To determine whether decision trees or Multi-Layer Perceptrons (MLP) are ideal in crop yield prediction, these models are compared with each other. Multi-layer perceptrons (MLP) are prominent among these techniques. Even though the MLP model was more accurate, decision trees also are relevant to the prediction process. This means have the capability of understanding multi-layer intra-data intricacies through their structure whereas decision trees may overfit on noisy data or grow too deep hence leading to many splits also known as being bushy unless they are pruned to reduce this bushiness. The study suggests a novel method for predicting agricultural productivity using a machinelearning model Decision Tree and Multi-Layer Perceptrons (MLP). A web interface is also created to enable smooth communication with the prediction model. Through the usage of this interface, farmers and agr
The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machinelearning approaches to identify such spatio-temporal relations from ...
The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimi...
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This paper presents a groundbreaking Smart Street Lighting System utilizing LoRaWAN technology to overcome the challenges of scalability, energy inefficiency, and communication limitations inherent in older smart stre...
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