arXiv AI June 16, 2026 By Shayan Abrar, Sudeepta Mandal, Abdul Awal Yasir, Sonjoy Bhattacharjee, Sadman Haque Bhuiyan, Samanta Ghosh, Rafi Ahamed

An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification

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arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.

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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.

By Rafi Ahamed, Md. Abir Rahman, Tasnia Tarannum Roza, Munaia Jannat Easha, Md. Asif Khan, Sudeepta Mandal

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arXiv AI

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PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification

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diffusion computer-vision safety

arXiv AI

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Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

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By Adri\'an C\'anovas-Rodriguez, Miguel A. Gonz\'alez-Ill\'an, Maria Fernanda Garc\'ia-Cruz, Pedro Nortes Tortosa, Jos\'e Salvador Rubio-Asensio, Miguel A. Zamora Izquierdo, Juan Antonio Mart\'inez Navarro, Antonio F. Skarmeta

fine-tuning benchmarks

arXiv Machine Learning

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Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

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.

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computer-vision

arXiv Machine Learning

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Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

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computer-vision benchmarks

Hugging Face Trending Papers

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Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques.

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CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification

PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification

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