Precision agriculture based on convolutional neural network in rice production nutrient management using machine learning genetic algorithm

Precision Agriculture technology can use high-tech sensors and analytical tools to help farmers better manage their crop yields, allowing them to demonstrate their techniques on the field. The greater use of remote sensing technologies in farm productivity and nitrogen management tools to increase yields and reduce operational costs contributes to reduced energy consumption. This concept describes the need to fulfill nutrient and crop production objectives for the near and long term. Based on our proposed approach, we concluded that the predictors of individual flow occurrences are variables linked to surface temperatures, moisture, and nitrogen availability. We have developed an automated Convolutional Neural Network creation approach genetic algorithms approach to handling image classification problems more efficiently. The best feature of the method is that it is “automated” and doesn't need domain knowledge about CNNs. When compared to conventional encoding, we compare the performance and adaptability of our processing. Moreover, the comparison of individual Genetic algorithms with CNN schemes employed for training the systems was compared to that of other GENCNN schemes ResNet-34, ResNet-50, AlexNet, DenseNet, VGGNet in terms of convergence speed and overall accuracy, among other things. Our studies data indicate that our Soil Nitrogen Management significantly reduced commercial system costs while providing notable outcomes. The experimental results of soil nutrient deficiency 99%and 97% show that the suggested method may be used to automatically identify the most efficient GENCNN model compared to existing models in a wide range of domains.