The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using non-task-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications.

The global market for Artificial Intelligence (AI) in Precision Farming is experiencing robust growth, projected to reach $577.2 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 18.1% from 2025 to 2033. This expansion is driven by several factors. Firstly, the increasing need for enhanced crop yields and efficient resource management in agriculture is fueling the adoption of AI-powered solutions. Precision farming techniques, enabled by AI, optimize irrigation, fertilization, and pest control, leading to significant cost savings and improved profitability for farmers. Secondly, advancements in machine learning, computer vision, and sensor technologies are providing more sophisticated and accurate data analysis, enabling predictive modelling for crop health, weather patterns, and yield estimation. The integration of AI with various farm equipment, such as drones and tractors, further streamlines operations and improves efficiency. Finally, growing government support and initiatives focused on promoting technological advancements in agriculture are fostering innovation and market expansion. Key segments driving growth include machine learning algorithms for data analysis and computer vision for image-based crop monitoring, particularly within the farm management and private planting applications. Major players like Microsoft, IBM, John Deere, and numerous agricultural technology startups are actively contributing to this dynamic market landscape, developing and deploying cutting-edge AI solutions for farmers worldwide. The regional distribution of the market is expected to reflect global agricultural trends, with North America and Europe likely to hold substantial market shares due to advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region, particularly India and China, is predicted to experience significant growth in the coming years owing to the large agricultural sector and increasing investments in agricultural technology. The "Others" segments in both type and application will likely experience substantial growth as AI technology matures and finds new applications in addressing specific agricultural challenges and niche markets. Further market penetration will depend on factors such as the affordability and accessibility of AI-powered tools, along with farmer training and digital literacy initiatives. While some restraints remain, such as the high initial investment costs and data security concerns, the long-term potential for AI in precision farming remains substantial.

The global Agricultural IoT Software market is experiencing robust growth, driven by the increasing need for precision agriculture and data-driven decision-making in the farming sector. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of smart farming technologies, including sensors, drones, and GPS, is generating vast amounts of data that require sophisticated software for analysis and interpretation. Secondly, the growing awareness among farmers of the benefits of data-driven insights for optimizing resource utilization (water, fertilizers, pesticides), improving crop yields, and reducing operational costs is accelerating software adoption. Thirdly, government initiatives and subsidies promoting digital agriculture in various regions are further stimulating market growth. The segment of Agricultural Big Data Monitoring Software holds a significant share, given the substantial amount of data generated by modern agricultural operations. Geographically, North America and Europe currently dominate the market, owing to advanced agricultural infrastructure and higher technology adoption rates. However, regions like Asia Pacific, particularly India and China, are emerging as high-growth markets, driven by rising agricultural production and increasing government support for technology adoption. Despite this positive outlook, challenges remain, including high initial investment costs for software and infrastructure, concerns about data security and privacy, and the digital literacy gap among farmers in some regions. The competitive landscape is dynamic, with both established technology companies like Huawei and Bosch and specialized agricultural technology firms like Cropin and AgriOpenData vying for market share. The market is witnessing strategic partnerships and mergers & acquisitions, indicating a trend towards consolidation. Further segmentation within the market includes applications (field planting, fruit & vegetable cultivation, aquaculture, poultry farming, grain storage) and software types (monitoring, decision-making, product management). Future growth will depend on the development of user-friendly, cost-effective software solutions that address the specific needs of diverse farming practices, coupled with initiatives to enhance farmer training and digital literacy. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into agricultural IoT software is poised to further revolutionize the sector, offering even more precise insights and predictive capabilities.

The Cauliflower Leaf Disease Dataset is a curated collection of high-quality images designed for machine learning and deep learning applications in plant disease detection. The dataset comprises 2,661 images categorized into three classes: Healthy (934), Insect Hole (639), and Black Rot (1,088). The images are collected under varying lighting conditions and angles to enhance model generalization.

Key Features: Healthy Leaves (934): Images of fresh, disease-free cauliflower leaves. Insect Hole (639): Leaves showing visible insect damage, such as holes caused by pests. Black Rot (1,088): Leaves affected by Xanthomonas campestris pv. campestris, a bacterial infection causing blackened veins and necrotic lesions.

Applications: Computer Vision: Image segmentation, feature extraction, and object detection for plant pathology studies. Machine Learning: Traditional classifiers (SVM, Random Forest) and feature engineering techniques for automated classification. Deep Learning: Convolutional Neural Networks (CNNs), Transfer Learning (ResNet, VGG, EfficientNet), and Explainable AI (Grad-CAM) to identify disease patterns. Agricultural Decision Support: Real-time disease monitoring, precision farming applications, and smartphone-based diagnosis for farmers.

This dataset is a crucial resource for researchers working on AI-driven plant disease identification and can contribute to the advancement of precision agriculture and sustainable farming solutions.

The AI data labeling services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across various sectors. The market's expansion is fueled by the critical need for high-quality labeled data to train and improve the accuracy of AI algorithms. While precise figures for market size and CAGR are not provided, industry reports suggest a significant market value, potentially exceeding $5 billion by 2025, with a Compound Annual Growth Rate (CAGR) likely in the range of 25-30% from 2025-2033. This rapid growth is attributed to several factors, including the proliferation of AI applications in autonomous vehicles, healthcare diagnostics, e-commerce personalization, and precision agriculture. The increasing availability of cloud-based solutions is also contributing to market expansion, offering scalability and cost-effectiveness for businesses of all sizes. However, challenges remain, such as the high cost of data annotation, the need for skilled labor, and concerns around data privacy and security. The market is segmented by application (automotive, healthcare, retail, agriculture, others) and type (cloud-based, on-premises), with the cloud-based segment expected to dominate due to its flexibility and accessibility. Key players like Scale AI, Labelbox, and Appen are driving innovation and market consolidation through technological advancements and strategic acquisitions. Geographic growth is expected across all regions, with North America and Asia-Pacific anticipated to lead in market share due to high AI adoption rates and significant investments in technological infrastructure. The competitive landscape is dynamic, featuring both established players and emerging startups. Strategic partnerships and mergers and acquisitions are common strategies for market expansion and technological enhancement. Future growth hinges on advancements in automation technologies that reduce the cost and time associated with data labeling. Furthermore, the development of more robust and standardized quality control metrics will be crucial for assuring the accuracy and reliability of labeled datasets, which is crucial for building trust and furthering adoption of AI-powered applications. The focus on addressing ethical considerations around data bias and privacy will also play a critical role in shaping the market's future trajectory. Continued innovation in both the technology and business models within the AI data labeling services sector will be vital for sustaining the high growth projected for the coming decade.

This dataset is designed for the detection and classification of diseases affecting carambola (starfruit) leaves and fruits using image-based analysis. Captured using a smartphone, it consists of high-quality images covering both healthy and diseased samples. The dataset can be utilized for training deep learning models in plant disease detection, classification, and agricultural monitoring applications.

Original Dataset: - Number of images: 2,618 - Data format: .jpg

Processed Dataset: - Number of images: 2,618 - Data format: .jpg

Augmented Dataset: - Number of images: 15,000 - Data format: .jpg

Augmentation Techniques: 1.Rotation, 2. Flipping, 3. Brightening, 4. Contrast Adjustment, 5. Blurring, 6. Shearing, 7. Zooming, 8. Adding Noise

Applications: - This dataset can be used for plant disease detection and classification, enabling agricultural monitoring using AI-based models.
- It supports image-based plant health assessment and can be applied in precision farming and crop management.
- It can be used to train deep learning models for improved disease detection and classification accuracy.

Applied AI in Agriculture Market Analysis The global applied AI in agriculture market is anticipated to grow exponentially in the coming years, with a market size of over USD 19942.01 million in 2025 and a CAGR of XX% during the forecast period 2025-2033. This growth is primarily driven by the increasing need for efficient farming practices, growing adoption of precision farming techniques, and rising government initiatives to promote smart agriculture. Key trends shaping the market include the integration of AI with IoT devices, the development of advanced algorithms for data analysis, and the increasing use of drones and sensors for data collection. Market Dynamics and Segmentation The applied AI in agriculture market is segmented based on technology, offering, application, company, and region. Major technology segments include machine learning, computer vision, and predictive analytics. The software and AI-as-a-Service segments dominate the offerings market. Drone analytics and precision farming are the prominent applications of AI in agriculture. Key market players include Microsoft, IBM, Google, Amazon.com, Inc., Deere & Company, TechTarget, Vision Robotics Corporation, DroneDeploy, PrecisionHawk, and AGCO Corporation. Regionally, North America and Europe are expected to hold the largest market shares, while Asia Pacific is projected to grow at the highest CAGR during the forecast period. The market is also influenced by factors such as the limited availability of skilled workforce, data privacy and security concerns, and the high cost of implementation. Recent developments include: May 2022, The Alliance for a Green Revolution in Africa (AGRA) and Microsoft have expanded their partnership to advance digital agricultural transformation in Africa. The partnership, which was formalized through a Memorandum of Understanding (MoU) signed on the sidelines of the World Economic Forum in Davos in 2022, builds on previous work between AGRA and Microsoft that focused on supporting AGRA's digital transformation efforts to improve food security in Africa., December 2022 Amazon Web Services (AWS), a subsidiary of Amazon.com, Inc., announced eight new capabilities for Amazon SageMaker, its fully managed machine learning (ML) service. Amazon SageMaker allows developers, data scientists, and business analysts to build, train, and deploy ML models quickly and easily., September 2022 DroneDeploy and Corteva Agriscience announced a partnership to help farmers make better management decisions year-round. Corteva operates one of the world's largest agricultural drone fleets, and DroneDeploy is a leading provider of drone software. Together, the two companies will offer farmers a suite of tools that can be used to monitor crops, identify problems, and optimize yields.. Key drivers for this market are: . The growing adoption of cloud computing, . The increasing adoption of precision agriculture; . Driver impact analysis. Potential restraints include: . High Cost of artificial intelligence precision farming, . Restraint impact analysis. Notable trends are: Growing number of wholesalers are adopting cloud-native software is expected to drive market growth..

Market Size and Growth The global Smart AI Farming market is projected to expand significantly over the forecast period, with a CAGR of approximately 12%. This growth is primarily driven by the rising need for increased agricultural productivity, combined with the increasing adoption of advanced technologies in farming practices. The market is expected to reach a valuation of over USD 12 billion by 2033. North America and Europe are anticipated to dominate the market, owing to their early adoption of smart farming technologies. Asia-Pacific is also expected to witness substantial growth, driven by government initiatives and increasing investments in agriculture. Key Trends and Restraints The growing adoption of data analytics and IoT devices is a major trend in the Smart AI Farming market. These technologies enable farmers to collect and analyze real-time data on crop conditions, soil health, and weather patterns, allowing for more efficient and data-driven decision-making. Furthermore, the rise of precision agriculture and automation is expected to further contribute to market growth. However, the high cost of implementation and concerns over data privacy may restrain the market to some extent. Additionally, the need for specialized skills and training for farmers to effectively utilize smart farming technologies remains a challenge. Smart AI Farming: Revolutionizing Agriculture with Advanced Technologies The smart AI farming market is poised for exponential growth, driven by advancements in artificial intelligence, automation, and data analytics. This comprehensive report provides an in-depth analysis of the industry, highlighting key trends, drivers, challenges, and growth catalysts, as well as the leading players shaping the landscape.

The AI data labeling services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across diverse sectors. The market, estimated at $10 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching a market value exceeding $40 billion by 2033. This significant expansion is fueled by several key factors. The automotive industry relies heavily on AI-powered systems for autonomous driving, necessitating high-quality data labeling for training these systems. Similarly, the healthcare sector utilizes AI for medical image analysis and diagnostics, further boosting demand. The retail and e-commerce sectors leverage AI for personalized recommendations and fraud detection, while agriculture benefits from AI-powered precision farming. The rise of cloud-based solutions offers scalability and cost-effectiveness, contributing to market growth. However, challenges remain, including the need for high accuracy in labeling, data security concerns, and the high cost associated with skilled human annotators. The market is segmented by application (automotive, healthcare, retail, agriculture, others) and type (cloud-based, on-premises), with cloud-based solutions currently dominating due to their flexibility and accessibility. Key players such as Scale AI, Labelbox, and Appen are shaping the market landscape through continuous innovation and expansion into new geographical areas. The geographical distribution of the market demonstrates a strong presence in North America, driven by a high concentration of AI companies and a mature technological ecosystem. Europe and Asia-Pacific are also experiencing significant growth, with China and India emerging as key markets due to their large populations and burgeoning technological sectors. Competition is intense, with both large established companies and agile startups vying for market share. The future will likely witness increased automation in data labeling processes, utilizing techniques like transfer learning and synthetic data generation to improve efficiency and reduce costs. However, the human element remains crucial, especially in handling complex and nuanced data requiring expert judgment. This balance between automation and human expertise will be a key determinant of future market growth and success for companies in this space.

The digital farming market, valued at $3540.6 million in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 15.2% from 2025 to 2033. This significant expansion is driven by several key factors. Firstly, the increasing adoption of precision agriculture techniques, enabled by advancements in sensor technology, data analytics, and IoT connectivity, allows farmers to optimize resource utilization (water, fertilizers, pesticides) leading to higher yields and reduced operational costs. Secondly, the growing awareness of climate change and the need for sustainable agricultural practices is fueling demand for digital farming solutions that promote efficient resource management and minimize environmental impact. Furthermore, government initiatives promoting technological adoption in agriculture and the rising availability of affordable and accessible digital technologies are accelerating market growth. The market is segmented by type (software & services, hardware) and application (farmland & farms, agricultural cooperatives), with software & services currently dominating due to the increasing demand for data-driven decision-making tools. Major players like BASF, Bayer-Monsanto, DuPont, and Syngenta are actively investing in R&D and strategic partnerships to strengthen their market positions. Geographical distribution reveals strong growth across North America and Europe, driven by high technological adoption rates and established agricultural infrastructure. However, Asia-Pacific is expected to witness significant growth in the coming years due to its vast agricultural land and increasing government support for technological advancements in the sector. The restraints to market growth primarily include the high initial investment costs associated with digital farming technologies, the digital literacy gap among farmers in certain regions, and concerns regarding data security and privacy. However, these challenges are being addressed through government subsidies, training programs, and the development of user-friendly and affordable solutions. Future trends suggest an increasing integration of artificial intelligence (AI) and machine learning (ML) in digital farming to enhance predictive capabilities, improve decision-making, and automate farm operations. The rise of cloud-based platforms for data management and analysis will further simplify data access and collaboration among stakeholders in the agricultural value chain. The overall outlook for the digital farming market remains exceptionally positive, promising transformative advancements in agricultural productivity and sustainability.

Dataset Overview: This dataset contains images of mango leaves collected from various mango orchards to aid in deep learning-based disease detection. The images have been categorized into healthy and diseased classes. The dataset aims to support research on plant disease detection, particularly for mango orchards.

This dataset is divided into two parts due to file size constraints. This dataset is Part-1 of the full dataset and contains additional images of mango leaves affected by various diseases.

🔗 Related Datasets: 👉 Part-2 Dataset doi: 10.17632/j3bn63t4sp.3

Reference: Awoal, Robiul; Islam, Zubair; Sarower, Afjal (2025), “SAR-MLD1-2025: A High Quality Mango Leaf Dataset for Disease Classification (part2)”, Mendeley Data, V3, doi: 10.17632/j3bn63t4sp.3

Please ensure that both Part-1 & Part-2 are downloaded for full dataset access.

The global Agricultural Education Service Platform market is experiencing robust growth, driven by the increasing need for skilled agricultural professionals and the adoption of advanced technologies in farming. The market's expansion is fueled by several key factors: a growing global population demanding increased food production, the urgent need for sustainable agricultural practices to combat climate change, and the increasing penetration of internet and mobile technologies in rural areas, enabling access to online learning resources. The market is segmented by application (adult and child education) and platform type (comprehensive agricultural education, professional field education, and others). The comprehensive agricultural education platform segment currently holds the largest market share due to its holistic approach covering various aspects of agriculture, from basic farming techniques to advanced technological applications. The professional field education platform segment is expected to witness significant growth driven by the demand for specialized training in areas like precision farming, data analytics, and farm management. North America and Europe currently dominate the market due to established agricultural infrastructure and high technological adoption. However, the Asia-Pacific region is poised for significant growth in the coming years owing to its large agricultural sector and increasing investment in agricultural technology and education. Competition in the market is intense, with established players like Agri-Tech East, Farmers Edge, and AgriWebb focusing on technological innovation and expansion into new markets. The market is facing certain challenges, including the digital divide in rural areas, limited internet access in developing nations, and the need for effective curriculum development that caters to diverse learning styles and technological literacy levels. Addressing these challenges will be crucial for sustained market growth. Furthermore, the increasing focus on integrating artificial intelligence (AI), machine learning (ML), and big data analytics into agricultural education is transforming the sector and creating new opportunities. The market is expected to consolidate further, with mergers and acquisitions driving growth and innovation. The forecast period (2025-2033) anticipates sustained growth fueled by technological advancements, increased government initiatives supporting agricultural education, and a growing awareness of the importance of sustainable farming practices. Successful players will need to adapt to changing technologies, focus on delivering high-quality, engaging content, and cater to the specific needs of different agricultural regions and user groups.

The global agricultural intelligent systems market is poised for substantial growth, driven by increasing demand for enhanced crop yields, resource optimization, and precision farming techniques. The market, estimated at $15 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of approximately $45 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of IoT sensors, AI-powered analytics, and automation technologies in agriculture. Farmers are increasingly recognizing the potential of these systems to improve efficiency, reduce costs associated with labor, water, and fertilizers, and ultimately enhance profitability. Furthermore, government initiatives promoting sustainable agriculture and technological advancements in robotics and drone technology are further bolstering market growth. The market segmentation reveals significant opportunities within precision application technologies (e.g., variable rate technology, precision spraying) and in types such as farm management software and autonomous tractors. North America and Europe currently dominate the market, but the Asia-Pacific region is projected to witness the fastest growth due to expanding arable land and increasing government support for technological advancements in agriculture. Despite the promising growth trajectory, the market faces certain restraints. High initial investment costs associated with adopting intelligent systems can pose a barrier to entry for smallholder farmers. Data security and privacy concerns related to the collection and use of agricultural data also require careful consideration. Furthermore, the lack of skilled workforce capable of operating and maintaining complex agricultural technologies presents a challenge for widespread adoption. Overcoming these hurdles through targeted government support programs, accessible financing options, and comprehensive training initiatives will be crucial for maximizing the market's full potential. The integration of advanced technologies like machine learning and edge computing will further drive innovation and address some of these challenges, leading to more user-friendly and cost-effective solutions in the future.

Artificial intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (Solanum lycopersicum), eggplant (Solanum melongena), cucumber (Cucumis sativus), bitter gourd (Momordica charantia), snake gourd (Trichosanthes cucumerina), ridge gourd (Luffa acutangula), ash gourd (Benincasa hispida), and bottle gourd (Lagenaria siceraria). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.

Smart Agriculture Solution Market Outlook

The global smart agriculture solution market size was valued at approximately USD 13.6 billion in 2023 and is projected to reach USD 27.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2032. This impressive growth can be attributed to increasing demand for sustainable farming practices, advancements in technology, and the growing need for efficient crop management techniques. Additionally, the integration of IoT, AI, and big data analytics is revolutionizing the agricultural sector by enabling real-time monitoring and data-driven decision-making.

One of the key growth factors driving the smart agriculture solution market is the rising global population, which necessitates an increase in food production. With traditional farming methods proving inadequate to meet the growing demand, smart agricultural solutions provide efficient alternatives. Precision farming, for instance, allows for optimal use of resources such as water and fertilizers, thereby enhancing crop yields and reducing waste. Moreover, the adoption of smart technologies can help farmers monitor crop health, weather conditions, and soil quality, ensuring timely interventions and better productivity.

Technological advancements are another significant driver of market growth. Innovations in IoT, AI, and robotics are transforming the way farming operations are conducted. IoT devices and sensors enable continuous monitoring of crops and livestock, offering real-time data that can be used to make informed decisions. AI algorithms can analyze this data to predict crop yields, identify pest infestations, and recommend the best farming practices. Robotics, on the other hand, automate labor-intensive tasks such as planting, watering, and harvesting, thereby increasing efficiency and reducing labor costs.

Government initiatives and supportive policies are also playing a crucial role in the expansion of the smart agriculture solution market. Many governments around the world are promoting the adoption of smart farming technologies through subsidies, grants, and training programs. These initiatives aim to modernize the agricultural sector, improve food security, and promote sustainable farming practices. For example, the European Union's Common Agricultural Policy (CAP) has allocated significant funds to digitize farming operations across member states, thereby boosting the adoption of smart agriculture solutions.

From a regional perspective, North America and Europe are leading the way in the adoption of smart agriculture solutions, driven by high levels of technological adoption and substantial investments in R&D. However, Asia Pacific is expected to witness the fastest growth during the forecast period, owing to the increasing demand for food production and the rising awareness about the benefits of smart farming practices. Countries like China, India, and Japan are investing heavily in agricultural technology to enhance productivity and ensure food security.

Component Analysis

In the smart agriculture solution market, the component segment is categorized into hardware, software, and services. The hardware segment includes sensors, drones, GPS systems, and other physical devices that are crucial for collecting and transmitting data. This segment is expected to hold a significant market share due to the growing adoption of advanced farming equipment. Sensors, for instance, are widely used for soil monitoring, weather tracking, and crop health assessment. They provide real-time data that helps farmers make informed decisions, thereby improving crop yields and reducing resource wastage.

The software segment comprises various applications and platforms that analyze the data collected by hardware devices. This includes farm management software, yield mapping, and data analytics tools. The software segment is anticipated to grow rapidly due to the increasing need for data-driven decision-making in farming operations. Advanced software solutions offer predictive analytics and real-time monitoring capabilities, enabling farmers to optimize resource use and enhance productivity. These solutions also facilitate better planning and forecasting, which are essential for efficient farm management.

Services in the smart agriculture solution market include consulting, system integration, and maintenance services. This segment is expected to witness substantial growth as farmers increasingly seek expert advice to implement and manage smart farming technologies. Consulting services help farmers choo

Dataset has a collection of 2,726 high-quality images of Table Grape leaves categorized into four distinct disease categories. Each category is stored in a separate folder such as, Downy Mildew, Bacterial Leaf Spot, Powdery Mildew, and Healthy Leaves. These images are Captured using mobile phones for real-world authenticity. Dataset with 256*256 image dimensions and 96 dpi Ideal for Machine Learning, Plant Pathology, and Agricultural Research. This dataset can be used for training AI models to recognize, classify, and diagnose table grape leaf diseases based on visual symptoms. It can also aid in developing automated plant disease detection systems for precision agriculture.

Problem Statement

👉 Download the case studies here

A large-scale agricultural enterprise struggled with monitoring crop health across vast farmlands. Traditional methods were labor-intensive and prone to delays in detecting diseases or resource deficiencies, leading to reduced yields and higher operational costs. The enterprise needed an advanced solution to monitor crop health, optimize farming practices, and enhance productivity.

Challenge

Implementing a smart agriculture system presented the following challenges:

Capturing and processing data from vast and diverse farmland efficiently.

Detecting early signs of crop diseases and nutrient deficiencies to prevent widespread damage.

Optimizing the use of resources such as water, fertilizers, and pesticides to reduce costs and environmental impact.

Solution Provided

A comprehensive smart agriculture and crop monitoring system was developed using drones equipped with computer vision, IoT sensors, and AI analytics. The solution was designed to:

Provide aerial imagery of crops to identify health patterns and detect diseases or pests.

Monitor soil and weather conditions in real-time through IoT sensors.

Deliver actionable insights for precise resource allocation and farming decisions.

Development Steps

Data Collection

Deployed drones to capture high-resolution images of crops across the farmland. Installed IoT sensors to monitor soil moisture, temperature, humidity, and nutrient levels.

Preprocessing

Processed drone imagery to enhance features such as color, texture, and shape for accurate analysis. Standardized data from IoT sensors to ensure compatibility and reliability.

Model Training

Developed computer vision models to detect crop diseases, pest infestations, and growth anomalies. Built predictive analytics models to forecast optimal planting, irrigation, and harvesting times.

Validation

Tested the system on pilot farmlands to ensure accurate disease detection, resource optimization, and yield prediction.

Deployment

Implemented the solution across the enterprise’s farmland, integrating it with existing farm management systems for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models based on new data, improving detection accuracy and predictive capabilities.

Results

Increased Crop Yields

The system improved crop health and productivity, resulting in a 20% increase in overall yields.

Optimized Resource Usage

Precise monitoring and insights reduced water, fertilizer, and pesticide usage, lowering costs and environmental impact.

Early Detection of Crop Diseases

AI-driven disease detection enabled timely interventions, preventing widespread crop loss.

Enhanced Decision-Making

Farmers benefited from data-driven insights, allowing them to make informed decisions about planting, irrigation, and harvesting.

Scalable and Sustainable Solution

The system demonstrated scalability, adapting to various crop types and farm sizes, while supporting sustainable farming practices.

ABSTRACT To enhance speed and agility in interpreting physiological quality tests of seeds, The use of algorithms has emerged. This study aimed to identify suitable machine learning models to assist in the precise management of seed lot quality. Soybean lots from two companies were assessed using the Supplied Test Set, Cross-Validation (with 8, 10, and 12 folds), and Percentage Split (with 66% and 70%) methods. Variables analyzed through Tetrazolium tests included vigor, viability, mechanical damage, moisture damage, bed bug damage, and water content. Method performance was determined by Kappa, Precision, and ROC Area metrics. Classification Via Regression and J48 algorithms were employed. The technique utilizing 66% of data for training achieved 93.55% accuracy, with Precision and ROC Area reaching 94.50% for the J48 algorithm. Applying the cross-validation method with 10 folds resulted in 90.22% of correctly classified instances, with a ROC Area outcome like the previous method. Tetrazolium Vigor was the primary attribute used. However, these results are specific to this study's database, and careful planning is necessary to select the most effective application methods.

This dataset features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

The Smart Agriculture Service market is experiencing robust growth, driven by increasing demand for efficient and sustainable farming practices globally. Technological advancements in areas like IoT sensors, AI-powered analytics, and cloud computing are revolutionizing agricultural operations, leading to improved crop yields, reduced resource consumption, and enhanced food safety. The market is segmented by application (farmland, greenhouse, garden, others) and type (agricultural e-commerce, anti-counterfeiting food traceability, agricultural leisure tourism, agricultural information service), reflecting the diverse applications of smart agriculture technologies. Key players like Funlead, JFE Engineering, NEC, and Alibaba Cloud are actively shaping the market landscape through innovative solutions and strategic partnerships. While data on precise market size and CAGR is not provided, based on industry reports and the rapid adoption of these technologies, we can estimate the 2025 market size to be around $15 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected for the forecast period (2025-2033). This growth is fueled by factors such as increasing government initiatives promoting digital agriculture, rising consumer awareness of food safety and traceability, and the growing adoption of precision farming techniques. However, challenges remain, including the high initial investment costs associated with implementing smart agriculture technologies, the digital divide affecting smaller farmers in developing countries, and concerns around data security and privacy. The market's future trajectory will depend on overcoming these barriers through targeted investments in infrastructure development, farmer training programs, and the establishment of robust data governance frameworks. The continued integration of AI and machine learning is poised to further enhance the capabilities of smart agriculture solutions, driving efficiency and sustainability across the agricultural value chain. This will lead to the development of more precise and adaptable solutions catering to diverse agricultural needs across various regions globally. The continued expansion of 5G networks will play a key role in enabling the widespread adoption of real-time data-driven decision-making in smart agriculture.