Smart Organic Crop Rotation Methodology via Walrus-Optimized Morphable Schema Convolution Network with Precision Agriculture Data

Abstract

Crop rotation is one organic practice that is very important in agriculture, as it it has spectacular impacts in the fields of maintaining soils, controlling pests, and improving crop yields. However, traditional approaches to crop rotation may involve a fixed time table or heuristics which do not match the complexity and dynamics of agriculture production of the current world. Since these are more of a rigid approach they are unable to counter act real time changes in the soil and the environment hence resulting to ineffancies and poor management of crops. Approaches like XAI, LPIS, PAMICRM, and GBRT have helped in enhancing approaches to crop rotation but still has drawbacks. These methods are sometimes inflexible, slow in terms of computations, as well as inaccurate especially when dealing with large and diverse data. However, they are not optimally utilising the real-time precision agriculture data leading to a more inefficient crop rotation. In the context of those shortcoming, our research presents Smart Organic Crop Rotation Methodology by Walrus-Optimized Morphable Schema Convolution Network (W-MSConvNet). This new concept involves integration of two models: the Walrus Optimization Algorithm (WaO) and the Morphable Schema Convolution Network (MSConvNet) to develop an optimized strategy for crop rotation. Incorporating precise agriculture information in real-time and the M2R-ScaleNorm technique, W-MSConvNet further estimates crop rotation schedules flexibly, accurately, and efficiently as compared to the previous work on crop rotation schedules. The effectiveness of W-MSConvNet is demonstrated through extensive comparative analyses, where it significantly outperforms existing methods. Key performance metrics include accuracy (99.3%), precision (98.8%), recall (99.5%), F1-score (99.2%), prediction rate (98.9%), and computational efficiency (a 80% reduction in processing time). These results highlight W-MSConvNet's ability to optimize crop yields and maintain soil health, establishing it as a leading methodology in organic crop rotation and setting a new standard for sustainable agricultural practices.