An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification
arXiv:2606.14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers. Plant diseases reduce fruit yield and quality, and plants are more susceptible to other stresses when they are infected. The lemon leaf disease dataset contains 1354 images. The dataset has 9 classes. Among the 9 classes only one class is for healthy leaf, and the other 8 classes are leaf diseases. The dataset was split into training (70%), testing (15%) and validation (15%) sets after comprehensive preprocessing. Two pretrained models (InceptionV3 and MobileNetV2) were
The continuous advancements in deep learning techniques and availability of specialized datasets make it possible to apply sophisticated AI models to agricultural challenges like plant disease detection.
Improved plant disease detection directly addresses food security concerns and agricultural efficiency, offering significant economic benefits to farming communities and a more stable food supply for consumers.
The ability to accurately and early detect plant diseases using AI allows for more targeted interventions, reducing crop loss and the widespread application of pesticides, leading to healthier produce and more sustainable farming practices.
- · Farmers
- · Agricultural technology companies
- · Consumers
- · AI developers
- · Traditional diagnostic methods
- · Pesticide manufacturers (potentially)
Farmers can implement proactive measures to treat diseased plants, preventing widespread crop failure.
Increased crop yields contribute to greater food security and potentially lower food prices in affected regions.
The success in citrus disease detection could spur wider adoption of similar AI-driven precision agriculture solutions across various crops globally.
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Read at arXiv cs.AI