Artificial Intelligence for Pest and Disease Monitoring in Eucalyptus Plants: Systematic Literature Review

Eucalyptus plants, renowned for their economic and environmental significance, are cultivated globally. Despite their value, these plants are vulnerable to pest and disease attacks, impacting productivity and quality. Accurate and timely monitoring is required to control pests and diseases in eucalyptus plants. The conventional method of human-based direct observation for monitoring pests and diseases in eucalyptus plants is fraught with weaknesses. Therefore, efforts are needed to enhance the effectiveness and efficiency of monitoring pests and diseases in eucalyptus plants through artificial intelligence or AI technology. AI is used to automatically detect and classify pests and diseases in eucalyptus plants using machine learning or deep learning algorithms and image processing. This study aims to provide a comprehensive review of the use of AI for detecting pests and diseases in eucalyptus plants using the Systematic Literature Review (SLR) method. Through this approach, this study identifies, evaluates, and analyzes relevant literature on the research topic from various digital sources. This study also provides an overview of the latest developments, methods used, and results achieved, as well as challenges and opportunities in the field of AI research for detecting pests and diseases in eucalyptus plants.Eucalyptus merupakan salah satu jenis tanaman kehutanan yang banyak dibudidayakan di berbagai negara karena memiliki nilai ekonomi dan lingkungan yang tinggi. Namun, tanaman eucalyptus juga rentan terhadap serangan hama dan penyakit yang dapat menurunkan produktivitas dan kualitasnya. Pemantauan atau monitoring yang akurat dan tepat waktu diperlukan untuk mengendalikan hama dan penyakit tanaman eucalyptus. Monitoring hama dan penyakit tanaman eucalyptus secara konvensional dilakukan dengan cara observasi langsung oleh manusia, namun metode ini memiliki beberapa kelemahan. Oleh karena itu, perlu adanya upaya untuk meningkatkan efektivitas dan efisiensi monitoring hama dan penyakit tanaman eucalyptus dengan memanfaatkan teknologi kecerdasan buatan atau Artificial Intelligence (AI). AI dapat digunakan untuk melakukan deteksi dan klasifikasi hama dan penyakit tanaman eucalyptus secara otomatis dengan menggunakan algoritma pembelajaran mesin atau pembelajaran mendalam dan pengolahan citra. Tujuan dari penelitian ini adalah untuk menyajikan tinjauan komprehensif tentang penggunaan AI dalam mendeteksi hama dan penyakit tanaman eucalyptus dengan menggunakan metode Systematic Literature Review (SLR). Penelitian ini mengidentifikasi, mengevaluasi, dan menganalisis literatur yang relevan dengan topik penelitian dari berbagai sumber digital. Penelitian ini juga memberikan gambaran menyeluruh tentang perkembangan terkini, metode-metode yang digunakan, hasil-hasil yang dicapai, serta tantangan dan peluang yang ada dalam bidang penelitian AI untuk deteksi hama dan penyakit tanaman eucalyptus

Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants

Ornamental plant production constitutes an important sector of the horticultural industry worldwide and fungal infections, that dramatically affect the aesthetic quality of plants, can cause serious economic and crop losses. The need to reduce the use of pesticides for controlling fungal outbreaks requires the development of new sustainable strategies for pathogen control. In particular, early and accurate large-scale detection of occurring symptoms is critical to face the ambitious challenge of an effective, energy-saving, and precise disease management. Here, the new trends in digital-based detection and available tools to treat fungal infections are presented in comparison with conventional practices. Recent advances in molecular biology tools, spectroscopic and imaging technologies and fungal risk models based on microclimate trends are examined. The revised spectroscopic and imaging technologies were tested through a case study on rose plants showing important fungal diseases (i.e., spot spectroscopy, hyperspectral, multispectral, and thermal imaging, fluorescence sensors). The final aim was the examination of conventional practices and current e-tools to gain the early detection of plant diseases, the identification of timing and spacing for their proper management, reduction in crop losses through environmentally friendly and sustainable production systems. Moreover, future perspectives for enhancing the integration of all these approaches are discussed

Epidemiology of Fusarium oxysporum f. sp. cucumerinum in greenhouse cucumbers

Fusarium Bosporus f. sp. cucumerinum is the fungal pathogen responsible for Fusarium vascular wilt of cucumber. The options for managing Fusarium wilt in greenhouse cucumbers are limited by our poor understanding of the modes of survival and dissemination of the pathogen. Aerial dissemination of the pathogen was investigated following the development of a highly specific and sensitive quantitative real-time PCR assay that reliably detected as few as 100 Fusarium oxysporum f. sp. cucumerinum genome copies in environmental matrices. Numbers of both macroconidia and microconidia were variable in greenhouse air samples at different times of day. A potential relationship between fluctuation in relative humidity and spore number was found. While this shows that the pathogen can be aerially disseminated, airborne spores were unable to infect wound stem sites. These results suggest that aerial inoculum propagates and disseminates the pathogen, but that infection is primarily through the root after aerial spores are deposited on the soil surface. Aerial dissemination was also found to occur through insect vectors. Sciarid and shore flies could carry between 1 × 102 and 1 × 106 pathogen genome copies/individual. Experimentally, sciarid and shore flies acquired F. oxysporum f. sp. cucumerinum following exposures to agar cultures of the pathogen of up to 94 hours and were found to transfer the pathogen, resulting in disease expression in a glasshouse transmission trial

Understanding Trichoderma bio-inoculants in the root ecosystem of Pinus radiata

Oral presentation on understanding Trichoderma bio-inoculants in the root ecosystem of Pinus radiat

Detecção de animais em rodovias utilizando câmeras e aprendizado supervisionado

Trabalho de Conclusão de Curso (Graduação)A detecção de animais é fundamental em nossa sociedade, onde se pode detectar e rastrear animais e preveni-los de colisões com os veículos nas rodovias. As abordagens tradicionais incluem a construção de passagens de fauna, implantação de cercas reais ou virtuais, vigilância por vídeo e sistemas \textit{break-the-beam}. Com todas essas abordagens, é crucial destacar a importância de se ter uma validação eficiente para reduzir o número de falsa detecção. A partir de dados levantados e pontos já identificados de presença constante de animais, os mesmos denominados de pontos quentes (hotspots), foi implementado um algoritmo, que utilizando recursos de processamento de imagens e métodos de aprendizado de máquina, através de imagens, possibilita classificar o objeto detectado como um animal e se este animal venha ser um perigo para segurança do veículo em movimento na rodovia, como também a própria segurança do animal, assim o condutor do veículo será alertado da presença deste possível animal próximo a sua localização atual, dando oportunidade ao motorista de reagir a alguma situação perigosa e evitar possíveis acidentes de trânsito

Detection of the forest disturbance using UAV multispectral photogrammetry

Epidemic (calamitous) overpopulations of bark beetles (Scolytinae Latreille, 1804) caused by climate change and inappropriate tree species composition currently have the most negative impacts on the development of Europe's mixed and boreal forests. Epidemic overpopulations can significantly undermine forest health and cause economic losses. It is therefore essential to use appropriate methods for early detection of bark beetle disturbance. Multispectral remote sensing (RS) methods using unmanned aerial systems (UAS) represent a new option for contactless landscape monitoring providing quantitative information on vegetation health with high spatiotemporal resolution and therefore appear to be suitable for early detection of disturbance. The thesis focused on the validation of the use of UAS multispectral photogrammetry and image classification methods for the detection of individual forest disturbance stages caused by the spruce bark beetle (Ips typographus Linnaeus, 1758) at the level of individual trees for the study of disturbance dynamics. In this dissertation, all important aspects of detection were elaborated: analysis of the suitability of spectral bands for disturbance detection, radiometric calibration of multispectral cameras, automated segmentation of individual canopies from...V současné době nejvíce negativně ovlivňují vývoj smíšených a boreálních lesů Evropy epidemické (kalamitní) přemnožení kůrovců (Scolytinae Latreille, 1804) vlivem klimatické změny a nevhodné skladby dřevin, která mohou výrazně narušit zdravotní stav lesů a způsobit ekonomické ztráty. Proto je nezbytné použít vhodné metody pro včasnou detekci kůrovcové disturbance. Metody multispektrálního dálkového průzkumu země (DPZ) pomocí bezpilotních leteckých systémů (UAS) představují novou možnost bezkontaktního monitoringu krajiny poskytující kvantitativní informaci o zdravotním stavu vegetace s vysokým časoprostorovým rozlišením, proto se jeví jako vhodné i pro včasnou detekci disturbance. Disertační práce se zaměřila na ověření využití metod UAS multispektrální fotogrammetrie a klasifikace obrazu pro detekci disturbance lesa způsobené lýkožroutem smrkovým (Ips typographus Linnaeus, 1758) na úrovni jednotlivých stromů s rozlišením jednotlivých fází napadení pro studium dynamiky disturbance. V disertační práci byly rozpracovány všechny důležité aspekty detekce: analýza vhodnosti spektrálních pásem pro detekci disturbance, radiometrická kalibrace multispektrálních kamer, automatizovaná segmentace jednotlivých korun z fotogrammetrického mračna bodů (PPC) a klasifikace fází disturbance na úrovni jednotlivých...Department of Physical Geography and GeoecologyKatedra fyzické geografie a geoekologiePřírodovědecká fakultaFaculty of Scienc