Evaluating Soil Attributes and Recommendation of Crop Using KFSMOTEC Approach in CNN Model

Abstract

In modern agriculture, precise soil analysis and crop prediction are pivotal for optimizing agricultural practices and ensuring sustainable food production. This paper focuses on a comprehensive approach that integrates soil pH and nutrient detection with subsequent crop prediction, harnessing advanced image processing and machine learning techniques. First the input soil image is preprocessed. After preprocessing, Active contour models are utilized to accurately delineate the soil regions within input images. This segmentation is a fundamental step in subsequent PH and soil nutrient detection. For soil nutrient detection, an Extreme Learning Machine has been employed with Ensemble Kernels (EK-ELM). The goal of developing this novel EK-ELM method is to classify nutrients by   the result obtained by aggregating different kernel functions. All the kernel functions were optimized using a weighted Ensemble by prior knowledge. This robust machine learning algorithm effectively processes the segmented soil images, extracting essential nutrient information from the soil's visual properties. Furthermore, the novel approaches K-Fold cross validation & Synthetic Minority Over-Sampling Technique implemented with Convolutional Neural Network (KFSMOTECNN) which comes into play for crop prediction. KFSMOTECNN have demonstrated their prowess in image analysis tasks. Here, they analyze the detected soil pH and nutrient information alongside additional environmental factors. By learning intricate relationships between these variables, KFSMOTECNN facilitate accurate crop yield predictions, aiding farmers in selecting suitable crop varieties. The proposed KFSMOTECNN approach provides 99% accuracy, 98.5% sensitivity, 98.62% specificity, 98% precision, and 98.21% F1-score for real soil dataset. These results unequivocally establish the superiority of our proposed approach -based crop prediction method when compared to conventional approaches.