Ch Padmavathi

This collection features six peer-reviewed reviews on advances and in detecting and forecasting crop pests and diseases. The first chapter introduces the concept of machine learning to identify and diagnose crop diseases, focussing on the deep learning concept. The second chapter discusses recent advances in crop disease forecasting models, focussing on the application of precision agriculture technologies and Earth observation satellites to identify areas at risk of possible disease outbreaks. The third chapter explores the contribution of remote sensing in improving the ways in which plant health is monitored in response to exposure to biotic stresses, such as disease. The fourth chapter reviews how sensor technologies in combination with informatics and modern application technologies can contribute to more effective pest control. The fifth chapter assesses the role of decision support systems for pest monitoring and management through information technology, such as spectral indices and image-based diagnostics. The final chapter addresses key issues and challenges in pest monitoring and forecasting models, such as the limitation of some traps in attracting insects through the use of sex pheromones.

Les maladies r nales chroniques, commun ment appel es maladies r nales chroniques, sont des maladies r nales caract ris es par une fonction r nale anormale ou une d gradation de la fonction r nale qui prend beaucoup de temps, voire des mois, pour s'achever. Les maladies r nales chroniques sont souvent d couvertes lorsque les individus sont reconnus comme tant en danger d'obstacle r nal, ceux qui ont de l'hypertension ou du diab te, par exemple, sont test s, ainsi que ceux qui ont une famille de sang qui a une maladie r nale chronique en cons quence, un pronostic rapide est un l ment critique dans la lutte contre la maladie et la fourniture d'une th rapie efficace . Lestechniques de mise en oeuvre de l'apprentissage profond telles que CNN; RNN pour la maladie r nale chroniquesont sugg r es dans ce travail. En utilisant le moins de caract ristiques possibles, le r sultat final permet de pr voir si la personne est atteinte ou non de la maladie r nale chronique. Dans notre recherche, nous utiliserons des algorithmes tels que le r seau neuronal convolution (CNN) en tant que syst me existant et le r seau neuronal r current (RNN) en tant que syst me propos , et nous les comparerons en termes de pr cision . Les r sultats montrent que le r seau neuronal r current (RNN) est plus performant que le r seau neuronal convolution (CNN).

La malattia renale cronica, comunemente nota come malattia renale cronica, un tipo di malattia renale caratterizzata da una funzione renale anomala o da un'interruzione della funzione renale che richiede molto tempo o mesi per essere completata. Le malattie renali croniche vengono spesso scoperte durante le fasi in cui si riconosce il rischio di un ostacolo renale, ad esempio quando sisottopongono a test coloro che hanno la pressione alta o il diabete, cos comecoloro che hanno un familiare affetto da malattia renale cronica . In questo lavoro vengono proposte tecniche di implementazione dell'apprendimento profondo come CNN e RNN per la CKD . Utilizzando il minor numero di caratteristiche, il risultato finale prevede che la persona sia affetta o meno da CKD. Nella nostra ricerca utilizzeremo gli algoritmi della rete neurale a convoluzione (CNN) come sistema esistente e della rete neurale ricorrente (RNN) come sistema proposto e li confronteremo in termini di accuratezza. Dai risultati emerso che la rete neurale ricorrente (RNN) ha prestazioni migliori rispetto alla rete neuralea convoluzione (CNN).

Chronische Nierenerkrankungen, auch bekannt als chronische Nierenerkrankung, sind eine Art von Nierenerkrankung, die durch eine abnorme Nierenfunktion oder einen Zusammenbruch der Nierenfunktion gekennzeichnet ist, der ber einen langen Zeitraum oder ber Monate hinweg andauert. Chronische Nierenerkrankungen werden h ufig bei Personen entdeckt, bei denen die Gefahr eines Nierenleidens erkannt wird, z. B. bei Personen, die an Bluthochdruck oder Diabetes leiden, sowie beiPersonen, in deren Familie eine chronische Nierenerkrankung vork ommt, weshalb eine rasche Prognose f r die Bek mpfung der Krankheit und die Bereitstellung einer wirksamen Therapie entscheidend ist . In dieser Arbeit werdenDeep Learning-Implementierungstechniken wie CNN und RNN f r CKD vorgeschlagen. Mit den wenigsten Merkmalen kann das Endergebnis vorhersagen, ob die Person CKD hat oder nicht. In unserer Forschung werden wir Algorithmen wie das Convolution Neural Network (CNN) als bestehendes System und das Recurrent Neural Network (RNN) als vorgeschlagenes System verwenden und in Bezug auf die Genauigkeit vergleichen. Aus den Ergebnissen geht hervor, dass das rekurrente neuronale Netz (RNN) besser funktioniert als das neuronale Faltungsnetz (CNN).

This collection features five peer-reviewed literature reviews on decision support systems (DSS) in agriculture. The first chapter provides a review of DSS in agriculture, whilst addressing the key questions surrounding their use for farm soil and crop management. The different aspects of agricultural DSS design, implementation and operation are also discussed. The second chapter assesses the role of DSS for pest monitoring and management through information technology such as, remote sensing, GIS, spectral indices, image-based diagnostics, and phenology-based degree day models. The third chapter discusses the potential of implementing DSS within the growing mechanisation in greenhouses. It examines differences in development and application of deterministic explanatory and data-based models for real-time control and DSS. The fourth chapter explores the key issues associated with deploying DSS in precision agriculture, whilst also considering their human and social aspects. The chapter also considers how future research on DSS can be moulded to improve productivity in a precision agriculture setting. The final chapter outlines the importance of a participatory approach in DSS development, whilst also offering examples of climate-based DSS for crop and land management, pest and disease management, and livestock (dairy) management.