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26 articles for “Plant disease detection”
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Fungus Detection System
Abstract: This project identifies fungal pathogens in wet soil with sensors and gives an instant result through an Android app. It helps farmers avoid crop loss, increase yield, and encourage eco-friendly farming practices by enabling early detection and evidence-based decision-making for soil health management. Soil condition is most important to agriculture and environmental health. Having fungus in pre-maturity soil analysis can prevent crop damage and increase the yield. This project seeks …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 30–34 Read article
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Qualitative Detection of Active Compounds of Some Extracts: The Medicinal Plant
Abstract: This study aimed to prepare aqueous extracts of the seeds of the following plants (Apium, Coriandrum, Trigonella, Punica, Linum,, Cinnamon, and Ocimum) to detect the active compounds in them represented by (saponins, tannins, phenols, alkaloids, and glycosides). The study concluded that the presence of alkaloids in the extracts of Apium, Trigonella, Punica, and Ocimum, and their absence in the remaining extracts. Phenols were observed in the extracts of Trigonella, cinnamon, …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 14, Issue 3, 2024 · pp. 48–53 Read article
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Extraction and Compound Analysis of Bauhinia acuminata: Soxhlet Method and Identification
Abstract: In the present study, the extraction and compound analysis of Bauhinia acuminata using the Soxhlet method, coupled with identification techniques, were conducted. Bauhinia acuminata L., a member of the Leguminosae (Fabaceae) family in the Caesalpinioideae subfamily, is a shrub usually reaching a height of around 3 m. It is native to tropical Southeast Asia and commonly found in the tropical regions of India and China. Primarily grown for ornamental purposes, …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 11, Issue 2, 2024 · pp. 18–23 Read article
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Nanotechnology’s Emerging Frontiers
Abstract: Nanotechnology holds significant promise across diverse sectors, including agriculture, food processing, energy, transportation, and communication systems. This swiftly evolving domain revolves around the development of groundbreaking materials and devices that tackle intricate engineering and scientific hurdles across various sectors. In agriculture and food processing, nanotechnology unfolds a myriad of potentialities. Its potential extends to the development of various products such as nano-fertilizers, nano-herbicides, nano-pesticides, and nano-scale carriers. Additionally, it plays …
Published in Journal of Petroleum Engineering & Technology · Vol. 14, Issue 1, 2024 · pp. 14–23 Read article
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Deep Learning models for real time detection of crop diseases in the Maharashtra/Mumbai district
Abstract: This research project addresses the critical agricultural challenge of crop disease management in the Maharashtra region of India by leveraging modern deep learning techniques. The primary objective is to identify, implement, and compare the efficacy of various deep learning architectures—including Convolutional Neural Networks (CNNs), MobileNet, and EfficientNet—for the real-time classification of diseases in key crops such as cotton, soybean, and sugarcane. A custom dataset of agricultural images specific to Maharashtra's …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 2, 2026 · pp. 36–48 Read article
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Harvestify: ML Based Tool for Home Gardening and Farming
Abstract: This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system's architecture. The Crop Disease Detection module analyses …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 18–28 Read article