Performance Analysis of Precision Agriculture Management Using Fuzzy Control
Implementation Plan:
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Step 1: Initially we collect the Crop Recommendation Dataset and load the data into this system.
Step 2: Next, we perform data preprocessing to handle incomplete data by using Imputation technique and Z-score data normalization based on collected data.
Step 3: Then, we integrate the enhanced Fuzzy PID controllers with interval type-2, type-3 fuzzy logic to resolve the uncertainty in controlling agricultural tasks and increasing crop output based on collected data.
Step 4: We predict the agricultural yield and mineral prescription for crop quality using PSO-SVM Algorithm based on collected data.
Step 5: Next, we perform the Novel Hybrid Optimization (HYFCO-WTWO) technique to increase agricultural productivity while minimizing water, fertilizer, and input costs based on collected data.
Step 6: Then, we make decisions to guide precise, zone-specific actions—like irrigation, fertilization, and pesticide application—at the right time and place using Random forest algorithm based on collected data.
Step 7: Finally, we plot performance metrics for the following:
7.1: Number of epochs vs. Accuracy
7.2: Number of epochs vs. Precision
7.3: Number of epochs vs. RMSE
7.4: Number of epochs vs. F1-score
7.5: Number of epochs vs. Convergence speed
Software Requirements:
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1. Development Tool: Python 3.11.4 or above version
2. Operating System: Windows-10 (64-bit)
Dataset:
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Link: https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset
Note:
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1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Once implementation begins, modifications will not be feasible without prior input. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming, as post-implementation changes will not be accommodated.
2) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only, not a real time project.
We perform with an Existing Approach Ref 1 : Title :- Improving crop production using an agro‑deep learning framework in precision agriculture
