arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.
arXiv:2609.10469v1 Announce Type: new Abstract: Automated plant disease diagnosis is increasingly deployed on farmer-held devices in regions where agronomic expertise is scarce and network connectivi...
arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.
CoAtNet-DeepMoE is a lightweight Convolution‑Attention hybrid architecture that incorporates a DeepSeek Mixture‑of‑Experts to reduce parameters while maintaining high accuracy for tomato disease classification. The model achieves state‑of‑the‑art performance on Kaggle and PlantVillage datasets, reporting 99.80% accuracy on Kaggle and 99.83% accuracy on PlantVillage, all with only 2.47 million parameters. The source code will be released on GitHub.
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
The paper introduces a dataset‑centric benchmark for deep learning approaches to grape leaf disease classification and detection. It evaluates publicly available datasets on disease categories, annotations, acquisition conditions, and class distributions, and tests representative models across image‑level classification, region‑level classification, and object detection. Results reveal high accuracy on controlled datasets but significant performance drops on heterogeneous, real‑world data, especially in cross‑dataset transfer and object detection tasks.
arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.
In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE).
The paper presents an automated pipeline that uses unmanned aircraft systems (UAS) multispectral imagery and a Vision Transformer to detect wheat streak mosaic virus (WSMV) at the plant level. While the model achieved 89% accuracy on over 6,500 test patches using treatment-based labels, ELISA-based ground truth revealed significant label noise, indicating that the high accuracy was largely due to label bias rather than true disease detection. When evaluated against more reliable row‑level symptom severity and plant‑level ELISA labels, both deep learning and classical machine learning models showed limited generalization and weak separability between infected and mock‑inoculated plants, underscoring the importance of biologically grounded labels and realistic data conditions for UAS‑based disease detection.
TinyCNN is a lightweight convolutional neural network with only 193,190 trainable parameters designed for on‑device plant disease classification. It achieves 98.88% test accuracy on the 38‑class PlantVillage benchmark, outperforming larger models while consuming far less energy, memory, and cost. The study also explores knowledge distillation to further compress the model and evaluates cross‑dataset robustness, finding a significant performance drop when moving from PlantVillage to PlantDoc due to background‑driven shortcut learning.
arXiv:2509. 06625v3 Announce Type: replace-cross Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation.
arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...
PlantC2USeg is a deep transfer‑learning framework that uses cross‑scale consistency learning and an information‑restricted decoder to improve plant point cloud segmentation. It achieves state‑of‑the‑art performance on Soybean3D and ShapeNet Part, and demonstrates strong few‑shot generalization across species and sensing conditions. The method reduces the need for large annotated datasets and lowers adaptation overhead for new plant species.