Abstract :

Agricultural Productivity In Developing Economies Is Constrained By Fragmented Advisory Tools That Address Individual Aspects Of Farm Management In Isolation. Farmers, Researchers, And Extension Workers Require Integrated Platforms That Combine Soil Intelligence, Crop Selection Guidance, Environmental Awareness, Disease Surveillance, And Conversational Support Within A Single Coherent Workflow. This Paper Presents Crop Assist AI, A Flask-based Intelligent Farming Platform That Unifies Five Complementary Modules: (1) A Machine Learning Crop Recommendation Engine Trained On NPK, PH, Temperature, Humidity, And Rainfall Parameters Using Random Forest, Decision Tree, And Gradient Boosting Classifiers That Achieve 88–94% Accuracy On Standard Agricultural Datasets; (2) A Micronutrient Analysis Module That Classifies Iron, Zinc, Copper, And Boron Concentrations Against Agronomic Thresholds And Computes A Soil Health Index (SHI); (3) A Simulated IoT Environmental Monitoring Dashboard Generating Real-time Readings For Temperature, Humidity, Soil Moisture, Light Intensity, PH, And Nutrient Index Through A Background Thread; (4) A Roboflow-integrated Computer Vision Pipeline For Leaf Disease Detection; And (5) A GPT4All-powered Conversational Chatbot With Google Text-to-Speech (gTTS) Output And Browser Speech Recognition. Evaluated On Representative Agricultural Inputs, The System Confirms 100% Route Availability, 100% Micronutrient Classification Accuracy, And Successful End-to-end Prediction Across All Five Modules. Comparative Analysis Against Singlepurpose Crop Systems, Standalone Disease Detection Applications, And IoT-only Platforms Demonstrates That Crop Assist AI Is The Only Evaluated System To Provide All Eleven Assessed Capabilities Simultaneously. The Open-source, Zero-cost Implementation Serves As An Accessible Research Platform, Demonstration Tool, And Pedagogical Resource For Precision Agriculture Education.

Published:

02-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

145-148

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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