Enhancing soil data classification for cassava crop productivity using swarm robotics optimization (SRO) and convolutional neural networks

Introduction Increasing crop productivity and ensuring sustainability requires smart agricultural management. This study integrates Swarm Robotics Optimization (SRO) and Convolutional Neural Networks (CNN) to improve cassava crop soil data classification. Combining the two methods improves soil data analysis efficiency and precision. IoT sensors can measure soil data, including NPK levels, pH, humidity, and temperature, in real time. An Internet of Things (IoT) gateway transfers sensor data to a cloud service over Wi-Fi or LoRaWAN. The data is safely stored and accessible for cloud processing. Then, we'll classify the data using CNN's pattern recognition and feature extraction skills. However, CNN performance depends on the ideal parameter and architecture arrangement. Here, SRO matters. Objective Based on social insect behaviour, SRO actively searches the search space to optimize CNN settings. SRO's adaptive optimization can help CNN classify complex soil data. So, hybrid technique. The optimized convolutional neural network (CNN) model trained on enhanced soil data classifies soil accurately. We compare this SRO-optimized CNN to other machine-learning algorithms to demonstrate its efficacy. The soil data can help gardeners prune cassava crops accurately. Classifying soil types helps farmers allocate resources, improve crop health, and boost yields. This technique increases cassava output and promotes sustainability. Advanced soil data classification using Swarm Robotics Optimization and Convolutional Neural Networks is trustworthy. The improved model improves crop management and yields by informing agricultural decisions. Future studies will add environmental characteristics and include more crops to improve precision agriculture.