PlantMicro benchmark shows current VLMs achieve low accuracy (e.g. GPT-5 at 34.93% on pathogen classification) on fine-grained microscopic plant image tasks.
Plantseg: A large-scale in-the-wild dataset for plant disease segmentation
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CropVLM is a domain-adapted vision-language model that achieves 72.51% zero-shot crop classification accuracy and superior open-set detection performance on novel species without retraining.
DACIS-guided PMP pipeline prunes plant pathology models by 78% while retaining 92.3% accuracy and achieving 7 FPS on Raspberry Pi 4 using few-shot meta-learning.
A systematic survey of over 200 works on deep learning and AI techniques for crops, fisheries, and livestock in agriculture.
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Benchmarking Vision-Language Models for Microscopic Plant Image Understanding
PlantMicro benchmark shows current VLMs achieve low accuracy (e.g. GPT-5 at 34.93% on pathogen classification) on fine-grained microscopic plant image tasks.
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CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis
CropVLM is a domain-adapted vision-language model that achieves 72.51% zero-shot crop classification accuracy and superior open-set detection performance on novel species without retraining.
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Meta-Learning Guided Pruning for Few-Shot Plant Pathology on Edge Devices
DACIS-guided PMP pipeline prunes plant pathology models by 78% while retaining 92.3% accuracy and achieving 7 FPS on Raspberry Pi 4 using few-shot meta-learning.
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AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
A systematic survey of over 200 works on deep learning and AI techniques for crops, fisheries, and livestock in agriculture.