Deep neural network based interactive fuzzy Bayesian search algorithm for low‐cost smart farming automation model

Abstract

Summary One of the most significant factors that influence the globalized economy is agriculture. In order to address the requirement of increasing populations in terms of food necessities, modernizations and technological progressions in agriculture, it is necessary to implement a smart agricultural system. Various traditional techniques are still utilized by the farmers and their intuition in agriculture is not enough to furnish and deliver various issues namely soil management, plant disease identification, weed management, irrigation management, and so forth. Therefore, in this paper, a low‐cost smart farming automation system is evaluated and presented. Since the development of a smart farming automation system minimizes the labor cost and enhances agricultural production level, this paper proposes a deep neural network based Interactive fuzzy Bayesian search (DNN‐IFBS) algorithm for a low‐cost smart farming automation system. In addition to this, the crop water stress index (CWSI) is computed to determine the water status of the plant by employing solar irradiation, canopy temperature, and so forth. The data for analysis is collected simultaneously from three cameras containing image resolutions of about 650 × 460 pixels each. Two different plants namely the melon and sesame are utilized as datasets for experimentation. Finally, the performances of the proposed approach are determined according to the statistical performance measures namely accuracy, specificity as well as F‐measure. The comparative analysis is carried out to evaluate the effectiveness of the proposed system. From the evaluation results, the accuracy rate obtained for the proposed approach is 98.7%.

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

“…Numerous studies have succeeded in predicting agricultural yield, and AI can be observed as a good prediction method. AI comprises neural network models like the artificial neural network (ANN) (Esfe, Kamyab, and Toghraie 2022), deep neural network (DNN) (Sivaraj and Palanisamy 2022) and machine learning (ML) (Reddy and Kumar 2021) models like the random forest (RF) (Yang et al 2021), K-nearest neighbour (KNN) and support vector machine (SVM) (Dang et al 2021). There were many crop yield prediction algorithms based on ML, including deep reinforcement learning, hybrid convolutional neural network (CNN)-long short term memory (LSTM) architecture, hybrid MLR-ANN architecture (Mythili and Rangaraj 2021) and the CNN-Recurrent neural network (RNN) framework.…”

“…Numerous studies have succeeded in predicting agricultural yield, and AI can be observed as a good prediction method. AI comprises neural network models like the artificial neural network (ANN) (Esfe, Kamyab, and Toghraie 2022), deep neural network (DNN) (Sivaraj and Palanisamy 2022) and machine learning (ML) (Reddy and Kumar 2021) models like the random forest (RF) (Yang et al. 2021), K‐nearest neighbour (KNN) and support vector machine (SVM) (Dang et al.…”