In the realm of global agriculture

This dataset was made by augmenting optimum soil and environmental characteristics for crop growth

pH

Crop Variety Recommendation AI Market Outlook

As per our latest research, the global Crop Variety Recommendation AI market size reached USD 1.82 billion in 2024, with a robust growth trajectory supported by a CAGR of 18.5% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 9.87 billion. The rapid adoption of artificial intelligence in agriculture, driven by the need for increased productivity, sustainability, and precision in crop selection, is fueling this remarkable expansion. The integration of AI-based recommendations is transforming traditional farming methodologies, ensuring optimal crop choices tailored to specific climatic, soil, and market conditions.

The primary growth driver for the Crop Variety Recommendation AI market is the rising global demand for food security amidst changing climatic conditions and shrinking arable land. With the worldÂ’s population projected to surpass 9 billion by 2050, agricultural stakeholders are under increasing pressure to maximize yields and minimize resource wastage. AI-powered crop recommendation systems leverage vast datasets, including historical yield data, weather patterns, soil health, and pest prevalence, to provide actionable insights. This significantly enhances farmersÂ’ decision-making processes, enabling them to select crop varieties that are both high-yielding and resilient to biotic and abiotic stresses. The resulting improvements in productivity and profitability are compelling both smallholder and large-scale farmers to invest in AI-driven solutions.

Another critical factor propelling market growth is the increasing government and institutional support for digital transformation in agriculture. Many countries are launching initiatives and funding programs aimed at modernizing the agri-food sector, with AI and data analytics at the core of these strategies. Governments, in collaboration with research institutes and agri-tech companies, are investing in the development and deployment of intelligent crop recommendation platforms to support farmers in optimizing their crop selection, reducing input costs, and promoting sustainable practices. These public-private partnerships are accelerating the penetration of Crop Variety Recommendation AI, particularly in regions with significant agricultural output and export potential.

The proliferation of affordable smart devices and IoT sensors in rural and semi-urban areas is also contributing to the rapid adoption of AI-based crop recommendation systems. Farmers now have access to real-time data on soil moisture, nutrient levels, weather forecasts, and pest threats, all of which can be seamlessly integrated into AI platforms for more accurate recommendations. The convergence of AI with precision agriculture tools, such as drones and satellite imagery, is further enhancing the effectiveness of these solutions. As digital literacy improves and connectivity expands, even smallholder farmers in developing regions are increasingly able to harness the benefits of AI-driven crop variety recommendations, democratizing access to advanced agronomic expertise.

AI-Powered Crop Yield Forecasting is becoming an integral part of modern agriculture, offering unprecedented accuracy in predicting crop outputs. By leveraging machine learning algorithms and historical data, these systems can analyze variables such as weather patterns, soil conditions, and crop health to forecast yields with remarkable precision. This capability is crucial for optimizing resource allocation, planning harvests, and managing supply chains effectively. As farmers and agribusinesses increasingly rely on AI-driven insights, the ability to anticipate yield fluctuations and adapt strategies accordingly is enhancing resilience against climate variability and market volatility. The integration of AI-powered forecasting tools is not only improving productivity but also contributing to more sustainable agricultural practices by reducing waste and optimizing input use.

Regionally, Asia Pacific stands out as the fastest-growing market, driven by the regionÂ’s vast agricultural base, rapid digitalization, and proactive government policies supporting smart farming. North America and Europe follow closely, with established agri-tech ecosystems and high rates of technology adoption among comm

Cost analysis for prototype implementation for crop and fertilizer recommendation system.

Soil Classification Datasets

Please ensure to cite the paper when utilizing the dataset in a research study. Refer to the paper link or BibTeX provided below.

This repository contains comprehensive datasets for soil classification and recognition research. The Original Dataset comprises soil images sourced from various online repositories, which have been meticulously cleaned and preprocessed to ensure data quality and consistency. To enhance the dataset's size and diversity, we employed Generative Adversarial Networks (GANs), specifically the CycleGAN architecture, to generate synthetic soil images. This augmented collection is referred to as the CyAUG Dataset. Both datasets are specifically designed to advance research in soil classification and recognition using state-of-the-art deep learning methodologies.

This dataset was curated as part of the research study titled "An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations" by Farhan Sheth, Priya Mathur, Amit Kumar Gupta, and Sandeep Chaurasia, published in Engineering Applications of Artificial Intelligence.

Links

• Paper: An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations
• Code: Development Code (model and recommendation system)

Application produced by this research is available at:

• Agro Companion: Agro Companion
• Server Code: Agro Companion Server

Note: If you are using any part of this project; dataset, code, application, then please cite the work as mentioned in the Citation section below.

Dataset

Both dataset consists of images of 7 different soil types.

The Soil Classification Dataset is structured to facilitate the classification of various soil types based on images. The dataset includes images of the following soil types:

• Alluvial Soil
• Black Soil
• Laterite Soil
• Red Soil
• Yellow Soil
• Arid Soil
• Mountain Soil

The dataset is organized into folders, each named after a specific soil type, containing images of that soil type. The images vary in resolution and quality, providing a diverse set of examples for training and testing classification models.

Original Dataset Details

• Total Images: 1189 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Collected from various online repositories and cleaned for consistency.

CyAUG Dataset Details

• Total Images: 5097 images
• Image Format: JPG/JPEG
• Image Size: Varies
• Source: Generated using CycleGAN to augment the original dataset, enhancing its size and diversity.

Input and Output Parameters

• Input Parameters:
  • Image: The images of the soils (JPG/JPEG format).
  • Label: The labels are in the format 'soil types' (folder names).
• Output Parameter:
  • Classification: The predicted class (soil type) based on the input image.

Citation

If you are using any of the derived dataset, please cite the following paper:

@article{SHETH2025111425,
  title = {An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {158},
  pages = {111425},
  year = {2025},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2025.111425},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197625014277},
  author = {Farhan Sheth and Priya Mathur and Amit Kumar Gupta and Sandeep Chaurasia},
  keywords = {Soil classification, Crop recommendation, Vision transformers, Convolutional neural network, Transfer learning, Fuzzy logic}
}

Horticulture crop recommendation in saline soils

The Crop Inputs Control System market is evolving rapidly, driven by the increasing demand for efficient agricultural practices and sustainable food production. This market encompasses a range of technologies and solutions that enable farmers and agribusinesses to optimize the use of inputs such as fertilizers, pest

Authors:Ignacio Ciampitti1, Emmanuela van Versendaal1, Juan Francisco Rybecky1, Josefina Lacasa1, Javier Fernandez1, David Makowski2, and Gilles Lemaire31 Department of Agronomy, Kansas State University2 University Paris-Saclay, INRAE, AgroParisTech3 INRAEThis dataset contains the result of extracting data of plant biomass and plant N concentration from 36 papers with a total of 4454 observations for 19 major crop species from 16 countries around the globe. This dataset is relevant to develop more universal critical N dilution curves, help to pinpoint factors limiting plant N status (and N nutrition index, NNI), and lead to refine N recommendation for major crop species under broad scenarios of different genotypes, environment, and management (GxExM).For more information related to this dataset or codes, please contact the corresponding author at: ciampitti@ksu.eduFor cite the dataset, please use:Ciampitti, I., van Versendaal, E., Rybecky, J.F., Lacasa, J., Fernandez, J., Makowski, D., & Lemaire, G. A global dataset to parametrize critical nitrogen dilution curves for major crop species. figshare https://figshare.com/s/3e3f60fe55c1ef6ff62a (2022).

Low cotton productivity in the rainfed cotton grown in central India is attributed to abiotic (water and nutrients) and biotic (insect pests and diseases) stress. Nutrient stress can be overcome by providing nutrients in right amounts and at the right time when the plant needs the most. Field studies in cereal crops have demonstrated fertilizer recommendations by using the Nutrient Expert® (NE) decision support system to improve crop yields. However, such information in the case of the commercial crop, cotton, is scarce. Therefore, on-farm trials were conducted in three districts of Maharashtra, India during 2018–2020 with the hypothesis that the NE-based fertilizer recommendation would lead to higher cotton productivity and savings in fertilizer. Averaged over two seasons and locations, lint yield was significantly greater in the NE based than the recommended dose of fertilizer (RDF), soil test crop response (STCR), and farmers' practice (FP). Internal utilization efficiency (IE) did not differ among treatments for N (4.8 to 5.9 kg lint kg−1 nutrient uptake) and K (6.7 to 7.2 kg lint kg−1 nutrient uptake). With regard to the fertilizer P applied, the FP treatment had the least IE (17.0 kg lint kg−1 nutrient uptake) and was significantly lower than the other treatments. Partial nutrient balance (PNB) did not vary among treatments for applied fertilizer N. The FP treatment had PNB < 1 in case of fertilizer P and ~20 in the case of fertilizer K. This indicates farmers applied excess of P fertilizers. On the other hand, farmers in the region applied very small amount of K. Although the NE treatment had the highest cost of cultivation, net returns were the greatest followed by the STCR and RDF treatments. Our studies demonstrate that the NE-based fertilizer recommendation is not only productive, but also profitable.

Irrigator Pro is an expert system designed to provide irrigation scheduling recommendations based on scientific data resulting in conservation minded irrigation management. The success of Irrigator Pro for Peanuts created interest in other groups. A collaborative effort between the NPRL, Cotton Commission, University of Georgia, and the Peanut Foundation was established to create comparable models for cotton and corn. Irrigator Pro is an irrigation scheduling tool for peanuts, corn, and cotton developed by the USDA Agricultural Research Service National Peanut Research Lab. Irrigator Pro is an expert system designed to provide recommendations based on scientific data resulting in conservation-minded irrigation management while maintaining high yields. The Flint River Soil and Water Conservation District, with funding from USDA NRCS, partnered with the Peanut Lab and University of Georgia to develop a smartphone app and cloud-based platform for Irrigator Pro. The new version has been in beta testing for the last two crop seasons with a full launch planned for 2019. Irrigator Pro is a trusted tool by farmers, crop consultants, Extension agents, and researchers across the Southeast. The original version is a desktop software that requires manual reading of soil moisture sensors in the field and manual data entry. The new smartphone app and cloud platform have automated the data collection process, integrating remote upload of soil moisture and temperature data with the Irrigator Pro model through the app and cloud platform. Resources in this dataset:Resource Title: Website pointer to Irrigator Pro. File Name: Web Page, url: https://irrigatorpro.org/

This dataset includes the supplemental data for the Masters thesis "An improved understanding of phosphorus dynamics and fertility management in forage production systems" and consists of forage fertility and pot trial data.Forage fertility trial: Soil fertility is vital for crop yields, but forage fertility often receives less attention compared to annual cash crops. In Ontario, producers typically apply 100 lbs acre-1 of 19-19-19 (N-P-K) fertilizer to forages, though its benefits are unclear. With new forage varieties like Festulolium, Ontario's recommendations have not changed since the 1980s. A three-year field trial aimed to determine the effect of fertility on the yield, quality, and profitability of 19 forage entries available on the Ontario market. The trial included three fertility treatments: 1) no fertilizer (control), 2) one-time spring application of 19-19-19 NPK, and 3) fertilization based on OMAFRA guidelines, alongside 19 different forage entries. The OMAFRA-recommended fertilization yielded the best results consistently, with yield differences between this and other treatments growing over time. By year 3, 100 lbs of 19-19-19 did not improve yields compared to no fertilizer. Grass-legume mixtures were more cost-effective and productive than pure grass mixtures. Red clover-based mixtures initially yielded more, but alfalfa-based mixtures were more productive overall. Mixtures outperformed single species for pure grasses. Lower tissue potassium (K) levels suggest K's critical role in yield, emphasizing the need for regular soil testing.Pot trial: Phosphorus (P) fertility recommendations for agricultural systems should be re-evaluated to increase P use efficiency. Current recommendations are based on soil test P (Olsen STP) concentrations indicating plant-available inorganic P but exclude organic P which can be made available over time through biological and biochemical mineralization of soil organic matter. This study explores whether P rates should be adjusted based on soil organic carbon (SOC) a key soil health indicator that is influenced by land use history. The aim was to understand the influence of SOC and other soil properties on crop response to added P using Lolium multiflorum in soils with varying SOC concentrations and contrasting land use histories (annual cropland or pasture). A 14-week growth room experiment investigated yield and P uptake using seven distinct field soils and five P application rates. Yield was positively correlated with soil P supply rate (using anion exchange probes) but not soil test P suggesting increased P supply from organic P mineralization in high SOC soils. This research highlights the importance of various soil health indicators including soil organic carbon and role of PSR in denoting plant-available P supply.

The Circle Irrigation Systems market has emerged as a vital component of modern agriculture, significantly influencing crop production efficiency and water conservation. These systems utilize a rotating framework to distribute water evenly across circular plots of land, ensuring optimal hydration for various crops w

Soil and nutrient management is an integrated system to manage soils, nutrients, water and crops in a sustainable manner to optimise crop production and maintain/improve soil health. These Guidelines provide regionally relevant guidance as a key component of the Strategic Plan of Action for the ASEAN Integrated Food Security (AIFS) Framework. The ultimate objective of the AIFS Framework is to achieve food security of the region by promoting adaptive and resilient €˜climate - smart’ agricultural systems that underpin a productive and profitable rural sector, while maintaining the functional capacity of the soil resource to provide essential ecosystem functions (commonly described as €˜soil health’), including mitigation of emission of greenhouse gases. Climate - smart agricultural systems are necessarily underpinned by the principles of Good Agricultural Practices (GAP), and this dependency is acknowledged in the Guidelines.These Guidelines on Soil and Nutrient Management and accompanying policy recommendations comprise advice prepared for agricultural decision makers. These decision makers may be a group or person that has the authority to make or to influence policy decisions, whether as a Minister of Agriculture and Forestry in ASEAN or in a member state of ASEAN, a member of an ASEAN Sectoral Working Group on Crops (ASWGC), a project steering committee, or an authority mandated to manage soil and nutrients in the region, including international, regional, and national bodies. The policy recommendations serve to inform how science - based evidence and recommendations on climate - resilient soil and nutrient management can assist in making the best decisions on soil and nutrient management that contribute towards sustaining agricultural production and enhancing food security.These Guidelines are intended to provide guidance to the policy, planning and technical support services of government, but the requirements of these different end - user groups are diverse. Consequently a scoping analysis was undertaken of the issues and responses of these end-user groups so the Guidelines could be framed to meet their requirements

ABSTRACT The objective of this study was to compute and compare DRIS norms for grafted and non-grafted red bell pepper crops grown in semi arid climate conditions. DRIS norms were computed with 84 samples of first young mature leaves collected during developed stage. The experiment had a completely randomized block design, and the values obtained for each plant and each variable were considered as independent replicates. Thirty-two DRIS norms nutrient ratios and coefficients of variation (CV) were computed from N, P, K, Ca, Mg, Fe, Cu, Zn and Mn analytical results. The ratios P/N and K/N in grafted and non-grafted pepper plants, showed low CV and therefore may play a fundamental role in crop production according with the DRIS norms calculated, since as light modification in the nutrient concentration led to a significant change in the nutritional balance. In conclusion, DRIS norms are sensitive for grafted and non-grafted plants. This work may signify an improvement in the nutritional diagnosis of grafted and non-grafted red bell pepper in semi arid climate conditions under a shaded greenhouse.

Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.

Phase 1, proposed system is compared with SOTA [1] when applied to the extracted features, achieving the highest level of accuracy.

Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.

Means followed by the same letter within the same column are not significantly different at P < 0.05.

This work comprises the theoretical determination and validation of diagnostic standards for the analysis of saturated soil extracts for cut rose flower crops (Rosa spp.) growing in the Bogota Plateau, Colombia. The data included 684 plant tissue analyses and 684 corresponding analyses of saturated soil extracts, all collected between January 2009 and June 2013. The tissue and soil samples were selected from 13 rose farms, and from cultivars grafted on the 'Natal Briar' rootstock. These concurrent samples of soil and plant tissues represented 251 production units (locations) of approximately 10,000 m2 distributed across the study area. The standards were conceived as a tool to improve the nutritional balance in the leaf tissue of rose plants and thereby define the norms for expressing optimum productive potential relative to nutritional conditions in the soil. To this end, previously determined diagnostic standard for rose leaf tissues were employed to obtain rates of foliar nutritional balance at each analyzed location and as criteria for determining the diagnostic norms for saturated soil extracts. Implementing this methodology to foliar analysis, showed a higher significant correlation for diagnostic indices. A similar behavior was observed in saturated soil extracts analysis, becoming a powerful tool for integrated nutritional diagnosis. Leaf analyses determine the most limiting nutrients for high yield and analyses of saturated soil extracts facilitate the possibility of correcting the fertigation formulations applied to soils or substrates. Recommendations are proposed to improve the balance in soil-plant system with which the possibility of yield increase becomes more probable. The main recommendations to increase and improve rose crop flower yields would be: continuously check pH values of SSE, reduce the amounts of P, Fe, Zn and Cu in fertigation solutions and carefully analyze the situation of Mn in the soil-plant system.