AgriPredict: An Integrated Machine Learning Framework for Crop Price Forecasting and Leaf Disease Identification

Abstract

Agriculture remains the backbone of the Indian economy, yet farmers continue to face significant challenges including unpredictable crop prices, rampant plant diseases, and limited access to timely decision-support tools. This paper presents AgriPredict, an integrated machine learning framework designed to assist farmers and village officials (Talathis) through two primary modules: crop price forecasting and leaf disease identification with treatment recommendations. The crop price forecasting module leverages machine learning regression models trained on historical market data, weather patterns, and regional crop information to predict future prices, enabling farmers to plan sales strategies effectively. The disease identification module employs Convolutional Neural Networks (CNNs) to classify leaf diseases from uploaded images and recommends appropriate pesticide treatments, facilitating early intervention and reduced crop loss. The platform further integrates real-time weather forecasts and links to government agricultural schemes, providing a holistic decision-support environment. Experimental evaluation demonstrates high accuracy in both price forecasting and disease classification tasks. The system is designed with a user-friendly web interface accessible to users with limited technical expertise. AgriPredict contributes toward bridging the technological gap in Indian agriculture, promoting data-driven decisions that improve crop yield, reduce financial losses, and enhance overall agricultural productivity.

Plant phenotyping relevance

葉画像から植物の病害状態をCNNで分類する機能が統合フレームワークの主要モジュールとして開発・評価されており、植物表現型の取得が中心的です。

Code and data availability

The paper describes a CNN leaf disease model and crop price forecasting framework but provides no public dataset, code, model checkpoint, or supplement of its own. The only named data sources (PlantVillage, AGMARKNET) are cited prior/public generic resources, not paper-specific assets, and no availability statement or

No evidence-backed public reproduction asset is currently recorded.