Machine Learning for Integrated Farmer Advisory and Marketplace Systems: A Literature Survey

Abstract

Farmers have to decide what to plant, what to buy, what to do when the crop goes wrong, and when to sell. Most of the software we found covers one of these decisions and leaves the rest alone. This paper looks at 27 studies, mostly from 2022 to 2026, on crop recommendation, fertilizer and input recommendation, advisory tools and disease detection, crop price prediction, online marketplaces, and a few joined-up platforms. For each one we note the method, the main finding, and what it leaves out if a farmer wants help from planting to sale. Tree-based ensembles turn up often in crop and input work, but price forecasting has no clear winner: ridge regression, LSTM and XGBoost each came out ahead in different studies. A handful of joined-up platforms exist, though they are mostly prototypes judged on benchmark data, and the e-NAM studies we read measure prices and arrivals but say nothing about advising the farmer. We also use our own prototype to show why accuracy figures need a simple baseline and real data before anyone leans on them. The paper ends with the gaps we found and a few things worth trying next.

Introduction

The text presents a literature survey on the application of machine learning and artificial intelligence in agriculture, focusing on supporting farmers throughout the agricultural cycle, from crop selection to selling harvested produce.

Traditional farming involves several challenges, including selecting suitable crops, choosing fertilizers, identifying crop diseases, obtaining reliable agricultural advice and accessing fair market prices. Although machine learning has been applied to each of these areas, most existing systems operate independently, requiring farmers to use multiple applications.

The paper reviews 27 research studies published mainly between 2022 and 2026 and classifies them into six major areas:

1. Crop Recommendation: Machine learning algorithms such as Random Forest, XGBoost, Naive Bayes and neural networks are used to recommend suitable crops based on soil nutrients, weather and environmental conditions. Several studies report high accuracy, but their results cannot be directly compared because they use different datasets and evaluation methods.

2. Fertilizer Recommendation: Machine learning models analyse soil properties, crop types and environmental conditions to suggest appropriate fertilizers. However, existing systems rarely provide information about fertilizer availability, suppliers or prices.

3. Agricultural Advisory and Disease Detection: Chatbots provide farmers with agricultural guidance, while deep learning models identify plant diseases from leaf images. However, disease detection models often perform better under laboratory conditions than in real agricultural environments.

4. Price Prediction: Algorithms such as LSTM, ARIMA, XGBoost and Random Forest are used to forecast agricultural commodity prices. However, accurate price prediction remains challenging, and simple forecasting methods sometimes perform as well as advanced models. Existing systems also rarely provide practical sell-or-hold recommendations.

5. Online Agricultural Marketplaces: Platforms such as India's e-NAM connect farmers with buyers and improve access to electronic trading. However, challenges involving infrastructure, payment processing, buyer verification and dispute resolution remain.

6. Integrated Agricultural Platforms: Recent research has attempted to combine crop recommendation, disease detection, weather forecasting, price prediction and chatbot services into unified platforms. Nevertheless, most systems lack real-world farmer testing and integrated marketplace facilities.

Research Gaps Identified

The literature survey identifies several important limitations:

• Most agricultural applications provide individual services rather than a complete integrated solution.

• Fertilizer recommendations are not sufficiently connected to product availability and purchasing facilities.

• Disease detection models often lack accuracy under real-world field conditions.

• Price prediction systems do not adequately support farmers in deciding when and where to sell their produce.

• Existing integrated platforms lack sufficient real-world validation with farmers.

• Agricultural marketplaces require better payment security, buyer verification and dispute management.

• Model performance is sometimes exaggerated because of limited comparisons with simple baseline models.

Conclusion

We looked at 27 studies on crop recommendation, input recommendation, advisory tools and disease detection, price prediction, online marketplaces, and joined-up platforms. Tree-based ensembles are common in crop and input work, price forecasting has no agreed winner, advisory tools lean on chatbots, and the marketplace studies are mostly about e-NAM. Each area has been worked on, and a few platforms now join several of them. What we could not find was evidence from real farms, or a platform that adds a marketplace and a sell or hold layer. That is where we would look next. The survey is narrative and covers a limited set of papers, so there are probably relevant studies we missed.

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Copyright

Copyright © 2026 Disha D S, Disha H Jain, Dhruhi Atykar, Parth Rawat, Vanishree . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.