arXiv Machine Learning
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
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arXiv AI
Jun 15
CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
arXiv Computer Vision
Oct 19
A100: A Hybrid Vision Foundation Model for Agricultural Plant Image Analysis
A100 is a hybrid CNN‑Transformer vision foundation model trained on 3 million unlabeled agricultural images using self‑supervised techniques (DINO, iBOT, KoLeo). It achieves superior crop classification (95.37 % with a frozen backbone) and phenotyping accuracy, and after fine‑tuning reaches 99.90 % on 32‑class crop identification and 99.02 % on 44‑class disease detection. With only 1 % of labeled data, A100 attains 98.01 % plant classification accuracy, outperforming existing baselines and other agricultural foundation models by significant margins.
arXiv Computer Vision
Sep 17
CoAtNet-DeepMoE: A Convolution-Attention Hybrid with DeepSeek Mixture-of-Experts for Parameter-Efficient Tomato Disease Classification
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 AI
Jun 16
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.
arXiv Computer Vision
Sep 10
AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification
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 Machine Learning
Sep 18
TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis
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.