Browsing by Author "Francisco, Mauro de Jesus Manuel"
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- Classification of diseases and pests in agricultural crops: A systematic reviewPublication . Francisco, Mauro de Jesus Manuel; Ribeiro, Fernando Reinaldo; Metrôlho, J.C.M.M.; Dionísio, Rogério PaisPlant diseases and pests significantly influence food production and the productivity and economic profitability of agricultural crops. This has led to great interest in developing technological solutions to enable timely and accurate detection. This systematic review aimed to find studies on the automation of processes to detect, identify and classify diseases and pests in agricultural crops. The goal is to characterize the class of algorithms, models and their characteristics and understand the efficiency of the various approaches and their applicability. The literature search was conducted in two citation databases. The initial search returned 278 studies and, after removing duplicates and applying the inclusion and exclusion criteria, 48 articles were included in the review. As a result, seven research questions were answered that allowed a characterization of the most studied crops, diseases and pests, the datasets used, the algorithms, their inputs and the levels of accuracy that have been achieved in automatic identification and classification of diseases and pests. Some trends that have been most noticed are also highlighted.
- Development of a system for identification and geolocation of diseases, pests and anomalies in agricultural cropsPublication . Francisco, Mauro de Jesus Manuel; Metrôlho, José Carlos Meireles Monteiro; Ribeiro, Fernando Reinaldo GarciaAchieving high production and sustainability in agriculture requires quick detection and control of diseases, pests, and anomalies in agricultural crops. In this dissertation, a system built with machine learning approaches is presented that automatically detects and geolocates these problems in crops. Real-time data collection and analysis are made possible by the combination of a mobile app and a web-based Admin Panel. The main goals of the system are to automatically identify diseases from pictures of plant leaves, to register and geolocate anomalies found, and to give users comprehensive data visualizations. Through the integration of these features, the system provides means that can help in increasing crop yields, promoting sustainable farming methods, and improving agricultural administration. The development process involved a close examination of the current technologies and approaches, an analysis of the system requirements and a modular and scalable system design and development. The solution uses machine learning models for accurate disease identification together with ASP.NET MVC for web development and Flutter with Dart for Mobile Application development. Farmers and agricultural managers can benefit greatly from the system. Along with advancing agricultural technology, this effort has important ramifications for environmental sustainability and food security.