Rice Crop Disease Prediction Using Machine Learning Technique

Abstract

Crop yields are affected at large scale due to spread of unchecked diseases. The spread of these diseases is similar to the spreading of cancer in human body. But, unlike cancer these diseases can be identified at early stages through plant phenotyping traits analysis. In order to effectively identify these diseases, effective segmentation, feature extraction, feature selection and classification processes must be followed. Selection of the best combination for the given methods is very complex due to the presence of a large number of the aforementioned methods. Thereby disease prediction models are generally found to be ineffective. This paper proposes a highly effective machine learning-based formulation approach to select a proper classification process which improves the overall accuracy of crop disease detection with different dimensionality of plant dataset and included maximum features also. Hence, the proposed adaptive learning algorithm gives 99.2% accuracy compared to other techniques like Back-propagation Neural Network (BPNN), Convolutional Neural Network (CNN), and SVM.

Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture

“…For each dataset, the proposed anomaly detection model generates improved results. The exist-ing studies show several limitations while detecting the anomalies from the given input data, meaning that the detection outcomes are reduced Patel, B et al [43]. Figure 11a-d By comparing it with other existing studies Vatti, R. et al [42], the detection performance of the proposed AGRU model is enhanced due to its higher efficiency.…”

“…For each dataset, the proposed anomaly detection model generates improved results. The existing studies show several limitations while detecting the anomalies from the given input data, meaning that the detection outcomes are reduced Patel, B et al [43]. Figure 11a-d Here, the proposed AGRU is analyzed using previous models, such as attention LSTM, Mogrifier LSTM, transform with attentional LSTM, and RNN.…”

Application of the Fuzzy C-Means Method in Grouping Heart Abnormalities Based on Electrocardiogram Medical Records

“…In addition to Fuzzy C-Means, several computational method have been used to identify cardiac abnormalities in heart disorder issues such as the use of an expert system with a certainty factor approach that is built based on a person's expertise that has been adopted into an application [1] , [2], [3]. Also, data mining has been implemented for the various needs such as predicting heart disease [4], [5], [6], identifying the possibility of disease spread using an association rule approach [7] and supporting decisions for the diagnosis of heart disease [8]. On the other hand, Cognitive Map of Certainty (CCM) is used to assess the causality of cognitive maps using the certainty factor for heart failure [9].…”