Precision agriculture relies heavily on accurate image analysis for crop disease identification and treatment recommendation, yet existing vision-language models (VLMs) often underperform in specialized agricultural domains. This work presents a domain-aware framework for agricultural image processing that combines prompt-based expert evaluation with self-consistency mechanisms to enhance VLM reliability in precision agriculture applications. We introduce two key innovations: (1) a prompt-based evaluation protocol that configures a language model as an expert plant pathologist for scalable assessment of image analysis outputs, and (2) a cosine-consistency self-voting mechanism that generates multiple candidate responses from agricultural images and selects the most semantically coherent diagnosis using domain-adapted embeddings. Applied to maize leaf disease identification from field images using a fine-tuned PaliGemma model, our approach improves diagnostic accuracy from 82.2\% to 87.8\%, symptom analysis from 38.9\% to 52.2\%, and treatment recommendation from 27.8\% to 43.3\% compared to standard greedy decoding. The system remains compact enough for deployment on mobile devices, supporting real-time agricultural decision-making in resource-constrained environments. These results demonstrate significant potential for AI-driven precision agriculture tools that can operate reliably in diverse field conditions.
AgroBench: Vision-Language Model Benchmark in Agriculture
The University of Osaka（大阪大学）；Kyoto University（京都大学）；Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所 (AIST)）；University of Oxford（牛津大学）；OMRON SINIC X（欧姆龙思尼克斯）
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Indian Institute of Technology（印度理工学院）；The University of Tokyo（东京大学）
In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant pheno…
CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture
Jeonbuk National University（全北国立大学）；Core Research Institute of Intelligent Robots, Jeonbuk National University（全北国立大学智能机器人核心研究所）；Universitas Udayana（乌达亚纳大学）；Rural Development Administration, National Institute of Crop Science（农村振兴厅国家作物科学研究所）
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
MBZ University of AI（人工智能大学）；CECS, Australian National University（计算机科学与工程系，澳大利亚国立大学）；Computer Vision Laboratory, Linköping University（链接öping大学计算机视觉实验室）
Georgia Institute of Technology（佐治亚理工学院）；University of Georgia（佐治亚大学）
Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture. While Structure from Motion and Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field with low-cost image sensors, these algorithms fail to capture the dynamic nature of continuously growing crops. In this paper we propose a 4D reconstruction approach to crop monitoring, which employs a spatio-temporal model of dynamic scenes that is useful for precision agriculture applications. Additionally, we provide a robust data association algorithm to address the …
Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls …
Centre de Developpement de Technologies Avancees（先进技术开发中心）
Computer vision techniques have attracted a great interest in precision agriculture, recently. The common goal of all computer vision-based precision agriculture tasks is to detect the objects of interest (e.g., crop, weed) and discriminating them from the background. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight, causing losses to crop yields. Weed detection and mapping is critical for site-specific weed management to reduce the cost of labor and impact of herbicides. This paper investigates the use of color and texture features for discriminat…
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We i…
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Imperial College（帝国理工学院）；University of Sheffield（谢菲尔德大学）；University of Manchester（曼彻斯特大学）；University of Southampton（南安普顿大学）；Fudan University（复旦大学）；Iowa State University（爱荷华州立大学）
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated…
AgriDoctor: A Multimodal Intelligent Assistant for Agriculture
State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
INSA CVL（INSA中央卢瓦尔河谷校区）；University of Orleans（奥尔良大学）；PRISME EA 4229（PRISME EA 4229研究所）
Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to meet with the high yield and high quality crop production. Classification and recognition in Unmanned Aerial Vehicles (UAV) images are important phases for crop monitoring. Advances in deep learning models relying on Convolutional Neural Network (CNN) have achieved high performances in image classification in the agricultural domain. Despite the success of thi…
Politecnico di Torino（都灵理工大学）；Politecnico Interdepartmental Centre for Service Robotics（都灵理工大学跨部门服务机器人中心）；Università degli Studi di Torino（都灵大学）；Institute of Electronics, Computer and Telecommunication Engineering of the National Research Council of Italy（意大利国家研究委员会电子、计算机与电信工程研究所）
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are gr…
We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This comprehensive database allows to filter the datasets under user-defined specifications such as for exam…
Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This pa…
Indraprastha Institute of Information Technology, Delhi（德里英迪拉普拉斯塔信息技术学院）；M S Swaminathan Research Foundation（M·S·斯瓦米纳坦研究基金会）
The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution (HSTR) satellite images. We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. The F1-score is increased by 7% when using multispectral…
Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. However, high-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These maps are especially critical in times of crisis when decision makers need to rapidly design and enact agriculture-related policies and mitigation strategies, including providing humanitarian assistance, dispersing targeted aid, or boosting productivi…
Design and Implementation of FourCropNet: A CNN-Based System for Efficient Multi-Crop Disease Detection and Management
D Y Patil Agricultural and Technical University（D Y Patil农业技术大学）；SPSMBH’s New Institute Of Technology（SPSMBH新科技学院）；Dr. D. Y. Patil Institue of Engineering Management and Research（D.Y.帕蒂尔工程管理与研究所）
FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Annam.AI CoE, MoE, Indian Institute of Technology Ropar, Punjab, India（Annam.AI联合研究所、教育部、印度理工学院罗帕尔分校）；Department of Mathematical Sciences, Rajiv Gandhi Institute of Petroleum Technology, Jais, UP, India（数学科学系、拉贾吉安石油技术学院）；Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India（计算机科学与工程系、印度理工学院罗帕尔分校）
Ground referencing is essential for supervised crop mapping. However, conventional ground truthing involves extensive field surveys and post processing, which is costly in terms of time and labor. In this study, we applied a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google street view (GSV) images in two distinct farming regions: central Illinois and southern California. We demonstrated the feasibility and reliability of the new ground referencing technique further by performing pixel-based crop mapping with vegetation indices as the mode…
AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping
Jockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系金钟STEM实验室）；Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University, Beijing, China（北航航天智能科学与技术学院）；School of Remote Sensing and Information Engineering, Wuhan University, China（武汉大学遥感与信息工程学院）
India is agriculture based economy and sugarcane is one of the major crops produced in northern India. Productivity of sugarcane decreases due to inappropriate soil conditions and infections caused by various types of diseases , timely and accurate disease diagnosis, plays an important role towards optimizing crop yield. This paper presents a system model for monitoring of sugarcane crop, the proposed model continuously monitor parameters (temperature, humidity and moisture) responsible for healthy growth of the crop in addition KNN clustering along with SVM classifier is utilized for infectio…
Context-Aware Pesticide Recommendation via Few-Shot Pest Recognition for Precision Agriculture
Department of Computer and System Sciences, Visva-Bharati, Santiniketan, India（计算机与系统科学系，Visva-Bharati，Santiniketan，印度）；Faculty of Technology, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India（技术学院，Uttar Banga Krishi Viswavidyalaya，Cooch Behar，印度）
Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer beca…
Reducing the use of agrochemicals is an important component towards sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environ…
Image-based ground distance detection for crop-residue-covered soil
College of Mechanical and Electrical Engineering, Qingdao Agricultural University（青岛农业大学机电工程学院）；National Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”（塔什干灌溉与农业机械化工程师学院国立研究大学）；Yellow River Delta Intelligent Agricultural Machinery Equipment Industry Academy（黄河三角洲智能农机装备产业研究院）
Evaluation of UAV-Based RGB and Multispectral Vegetation Indices for Precision Agriculture in Palm Tree Cultivation
College of Engineering and IT, University of Dubai（迪拜大学工程与信息技术学院）；Department of Electronics and Communication, TKM College of Engineering（TKM工程学院电子与通信系）；Department of Electronics and Communication, MES College of Engineering（MES工程学院电子与通信系）；Applications Development and Analysis Section, Mohammed Bin Rashid Space Centre (MBRSC)（穆罕默德·本·拉希德航天中心应用开发与分析部门）
High efficiency in precision farming depends on accurate tools to perform weed detection and mapping of crops. This allows for precise removal of harmful weeds with a lower amount of pesticides, as well as increase of the harvest's yield by providing the farmer with valuable information. In this paper, we address the problem of fully automatic stem detection from image data for this purpose. Our approach runs on mobile agricultural robots taking RGB images. After processing the images to obtain a vegetation mask, our approach separates each plant into its individual leaves and later estimates …
