Lychee(Litchi) Leaf Health Condition and Growth Phase Image Dataset for Agricultural Research

Published: 17 October 2025| Version 2 | DOI: 10.17632/y9vcc354tt.2
Contributors:
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Description

This dataset presents a collection of lychee (Litchi chinensis) leaf images systematically collected from Feni and Brahmanbaria districts under natural field conditions of Bangladesh. It encompasses multiple health states and growth stages. This dataset contains 2582 images of lychee leaf images with 5 classes: Early_Bud, Young, Healthy, Senescent_Leaves, and Dried. The images were captured with smartphone in real agricultural environments. The dataset offers a valuable benchmark for machine learning and deep learning research, with applications in plant productivity, phenological analysis, and precision agriculture. This dataset aims to help farmers and researchers support sustainable lychee production and improved crop management strategies. Captured Using: Samsung Galaxy A36 (50 MP f/1.8 wide with OIS + 8 MP f/2.2 ultrawide + 5 MP f/2.4 macro) Dataset Classes and Image Counts: Early_Bud:500 images Young:540 images Healthy:542 images Senescent Leaves:500 images Dried:500 images Total images: 2582 Image Details: Original image resolution:3060 x 4080 Resized image resolution:420 x 560 Image formate:JPG Color mode:RGB Collection device: Smartphone camera Location: Feni and Brahmanbaria districts,Bangladesh

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Institutions

  • Daffodil International University

Categories

Computer Vision, Agricultural Economics, Image Processing, Machine Learning, Supervised Learning, Image Classification, Convolutional Neural Network, Deep Learning, Agricultural Biotechnology, Agriculture

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