Smart Agriculture and AI

Latest papers

• Integrated Deep Learning Framework Designed on Hybrid Optimization Strategies for Automated Health Detection and Analysis in Silkworms

  Komala K V, Lata B T, Venugopal K R · 29 September 2026

  A Hybrid Residual Attention Network is proposed for accurately classifying silkworm images into six different classes, including healthy and diseased states. It uses residual blocks for deep feature extraction and attention to focus on disease related features. A novel Integrated Adaptive Momentum O…

• AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture

  Shengjie Guo, Xin Li, Borjana Arsova, Hanno Scharr, Silvio Salvi · 25 September 2026

  Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary widely across species and imaging conditions. Exemplar boxes specify the target without category-specific retraining, and point predictions identify …

• RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery

  Kinjalk Parth, Sebastian Varela, Andrew D. B. Leakey · 23 September 2026

  A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions a…

• Combining Object Detection with Geometry-Aware Clustering to Distinguish Overlapping Plants in UAV Imagery

  Ik Jae Lee, Hieu D. Nguyen, Mahbubur Meenar, Carlos Morrison Martinez, Cameron Connelly · 21 September 2026

  Reliable plant-level information from unmanned aerial vehicle (UAV) imagery is important for automated crop monitoring. However, in dense crop canopies, adjacent plants frequently overlap and are detected as a single object, reducing the reliability of plant-level measurements. This study presents a…

• Configurable Multi-Stage Vision Pipeline for Crop Disease and Pest Diagnosis

  Naga Ganesh, Chandrashekar M S, Lakshmi Pedapudi, Aakash Singh, Vineet Singh · 21 September 2026

  Farmer.Chat is Digital Green's farm advisory service for smallholder farmers. When something looks wrong with a crop, the farmer takes a photograph and sends it, and that photograph is the whole question: no symptom described, no crop named, often no text at all. The service has to determine whether…

• PlantShade: Predicting Plant Shadows for Lighting-Aware Robotic Agricultural Operation

  Longchao Da, Xiaoou Liu, Xingjian Li, Lirong Xiang, Hua Wei · 21 September 2026

  Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent advances in frontier artificial intelligence have enabled scientific agriculture with strong potential to improve crop productivity. In this paper, we ide…

• AgriScope: Pixel-Grounded Multimodal Understanding for Agricultural Images

  Abderrahmene Boudiaf, Mohamad Alanssari, Irfan Hussain, Sajid Javed · 18 September 2026

  Agricultural image understanding requires fine-grained recognition of plant diseases, pests, crop structures, and botanical species under complex real-world conditions. Despite recent advances in Multimodal Large Language Models (MLLMs), existing models remain limited to text-only outputs and lack p…

• TinyCNN: A 193K-Parameter Network for On-Device Plant Disease Detection, with a Cross-Dataset Robustness Diagnosis

  Ngoc-Bao Ho-Lam, Thai-Anh Nguyen · 18 September 2026

  Detecting crop disease early is central to sustainable agriculture and food security under United Nations Sustainable Development Goal 2 (Zero Hunger), and is especially urgent in resource-constrained regions where expert diagnosis is scarce but low-cost mobile devices are widespread. This paper pre…

• Selective Cotton Boll Localization for Robotic Harvesting: Evaluation of Deep Learning Vision Models Under Field Conditions

  Thevathayarajh Thayananthan, Xin Zhang, Isuru Laddusinghe Badu, Jonathan Harjono, Glen C. Rains, Beiwen Li, Leonardo M. Bastos, Nuwan K. Wijewardane, Vitor S. Martins · 18 September 2026

  This study developed and evaluated a deep-learning-based perception framework for selective robotic cotton picking. The dataset contained 1,008 annotated field images collected using three cameras under varying natural lighting and weather conditions. Object-detection models from the YOLOv8 through …

• A Multi-Modal Generative Model for Tomato Disease Leaves Understanding

  Khang Nguyen Quoc, Minh-Phuoc Tran, Gia-Han Truong, Luyl-Da Quach · 18 September 2026

  Artificial intelligence for plant disease analysis has advanced from task-specific classifiers to multi-modal models capable of jointly interpreting visual and textual information. However, practical deployment in precision agriculture remains limited because most existing approaches treat disease u…

• Evaluating Mesh Reconstruction Methods for Crop Phenotyping

  Karanvir Singh, Theo Morales, Binh-Son Hua, Mukesh Saini · 16 September 2026

  Phenotyping an agricultural crop is crucial for studying its entire life cycle, as it provides vital insights to improve yield and, ultimately, food production. Doing the same for crops grown on remote sites is a challenge for the specialists who cannot be available on-site. 3D reconstruction techni…

• RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing

  Mehrad Mortazavi, David J. Cappelleri, Reza Ehsani · 14 September 2026

  Driven by the need to address labor shortages and meet the demands of a rapidly growing population, robotic automation has become a critical component in precision agriculture. Leaf-level hyperspectral spectroscopy is shown to be a powerful tool for phenotyping, monitoring crop health, identifying e…

• EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion

  Jiayi Li, Zihan Zhang, Erhankang Yan, Yitian Chen, Yuze Li, Chengzhang Ding, Jianxin Cao · 14 September 2026

  Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that are difficult to ob serve from overhead views. However, post-seedling maize fields create a difficult in stance segmentation setting: stems, leaves, ta…

• When Ground-Truth Fidelity Matters: An Orchestrated UAS Framework for Wheat Streak Mosaic Virus Detection Using Vision Transformers and Machine Learning

  Dewi Endah Kharismawati, Sandeep Dhakal, Courtney E. McCusker, Jennifer R. Wilson, Erik W. Ohlson, Sami Khanal · 14 September 2026

  Wheat streak mosaic virus (WSMV) is a destructive pathogen of sweet corn and other cereal crops, causing yield losses and complicating early detection because symptoms are spatially variable and subtle. In sweet corn seed production, WSMV also has regulatory importance, as phytosanitary regulations …

• LettuceVisSim: A Simulator That Generates Lettuce Image Time-series for Vision-Based Reinforcement Learning

  Ziye Zhu, Bert van 't Ooster, Congcong Sun, Eldert van Henten, Sjoerd Boersma · 14 September 2026

  Vision-based reinforcement learning holds strong potential for decision-making in controlled environment agriculture (CEA). However, its development is hindered by the scarcity of labelled crop images. To address this gap, LettuceVisSim, a lettuce growth simulator that generates labelled time series…

• Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering

  Sheikh Hasan Elahi, Rusith Chamara Hathurusinghe Dewage, Habib Ullah, Muhammad Salman Siddiqui, Rakibul Islam, Fadi Al Machot · 11 September 2026

  Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regression framework that combines Vision Transformer (ViT) feature embeddings, clustering-based task construction, and gradie…

• Precision in Rice Variety Classification using Stacking-Based Ensemble Learning

  Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, Mohammad Shorif Uddin · 10 September 2026

  Rice, a staple food for a significant portion of the global population, exhibits remarkable diversity in its varieties, presenting substantial challenges for accurate identification by consumers, traders, and farmers. This complexity often facilitates fraudulent practices, such as the unauthorized m…

• AgroVisNet: A lightweight Convolutional Network and the BD-PlantDX Expert-Validated Benchmark for Radish, Potato and Pointed Gourd Disease Classification

  Md. Abdullah Mandal, Saad Ahmed, Md. Khalid Syfullah · 10 September 2026

  Automated plant disease diagnosis is increasingly deployed on farmer-held devices in regions where agronomic expertise is scarce and network connectivity is unreliable. Three obstacles limit its practical value: public benchmarks are dominated by a small set of non-native crops, region-specific data…

• Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

  Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana · 10 September 2026

  Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species identification remains poor, and it is unclear whether this reflects weak visual features or a failure to connect them to domain knowledge. We build a …

• Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments

  Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang · 9 September 2026

  Smart agriculture platforms usually bundle AI diagnosis, IoT sensing and decision push into a single package, so the green benefit attributable to each component remains unclear and resource-allocation decisions lack quantitative evidence. Building on a previous platform-level Monte Carlo assessment…

• Monte Carlo-Based Ex-Ante Assessment of the Green Benefits of an AI-Driven Smart Agriculture Platform in Hainan

  Zhaoyang Li, Ruijie Zhang, Zhaoji Sun, Lu Zhang · 9 September 2026

  Smart agriculture platforms are widely regarded as key carriers for implementing China's pesticide and fertilizer reduction, water-saving and carbon-reduction agendas, yet a unified quantitative framework for assessing their green value is still lacking. Taking an AI-driven decision platform for tro…

• Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

  Mahadev Sunil Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith, Gangireddy Rahul Jogi, Sudheesh Manalil, Arnab Raha, Amitava Mukherjee, Parthasarathy Seethapathy, G. Gopakumar · 7 September 2026

  Chilli (Capsicum annuum) is one of India's most economically significant crops, yet its productivity is persistently threatened by diseases that are difficult to identify without expert intervention. While Vision Transformers (ViTs) have achieved high classification accuracy, their large computation…

• Concept of a Sensor Test Environment for Dusty Agricultural Conditions

  Peter Buckel, Johannes Hermann, Jonas Wollmann, Thomas Dietmueller, Timo Oksanen · 4 September 2026

  Dust in agriculture presents a significant challenge for autonomous agricultural machinery. Dust can impair the performance of sensors and algorithms. This work, therefore, presents a concept for a proving ground consisting of an indoor and outdoor area. The indoor area comprises a laboratory test b…

• DropClick: Semi-Automated One-Click Segmentation for Agricultural Robotic Data

  Patrick Zimmer, Michael Halstead, Chris McCool · 4 September 2026

  Labelling vision datasets, especially for segmentation tasks, is a laborious and costly process that stymies novel developments in agricultural robotics. In this paper, we present DropClick, a click-guided segmentation tool that simplifies the annotation process. Our system utilises single-click inp…

• Empowering Local Agriculture: A Deep Learning-Powered Web System for Identifying Bangladeshi Mango Varieties

  Monowar Islam, Safaruzzaman Shovo · 31 August 2026

  Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are often captured under varying real-world conditions. This work presents a deep learning-based web system for automatic identification of Bangladeshi mango…