Karnataka Soil

With the increase in world population, agricultural planning is significant to ensure food security. Timely recommendations for crops could be valuable for planning food production and maintaining food sustainability. This proposed work suggests a crop recommendation model considering physical soil characteristics, chemical soil characteristics, climate, and crop characteristics, using Improved Deep Belief Networks (IDBN). For this study, four important Indian crops—rice, maize, finger millet and sugarcane were taken into account. The soil, weather, and crop datasets were gathered from different sources and subjected to feature selections method to extract optimal features. The crop recommendation model was developed using the proposed IDBN with Gaussian Restricted Boltzmann Machines and Ranger Optimizer with selected optimal features as input. Gaussian Restricted Boltzmann Machines enable the use of continuous values, and Ranger Optimizer ensures rapid convergence with selected optimal features as input. The findings from the investigation confirm that the suggested IDBN outperforms conventional Deep Belief Networks (DBN). The effectiveness of the crop recommendation model was evaluated using IDBN, and its performance was compared with several other machine learning algorithms.

随着全球人口的增长，农业规划对于保障粮食安全至关重要。及时的作物推荐对于粮食生产规划和维持粮食可持续性具有重要价值。本研究提出一种作物推荐模型，该模型考虑土壤物理特性、化学特性、气候及作物特性，采用改进深度信念网络（Improved Deep Belief Networks, IDBN）构建。研究选取印度四种重要作物：水稻、玉米、龙爪稷（finger millet）和甘蔗进行分析。土壤、天气和作物数据集来源于不同渠道，并通过特征选择方法（feature selection method）提取最优特征。作物推荐模型基于所提出的IDBN构建，该模型采用高斯限制玻尔兹曼机（Gaussian Restricted Boltzmann Machines）和Ranger优化器（Ranger Optimizer），以选定的最优特征作为输入。高斯限制玻尔兹曼机支持连续值的使用，而Ranger优化器可确保快速收敛。研究结果证实，所提出的IDBN性能优于传统深度信念网络（Deep Belief Networks, DBN）。作物推荐模型的有效性通过IDBN进行评估，并将其性能与其他多种机器学习算法进行对比。