Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.

Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification

The South Indian mango industry is confronting severe threats due to various leaf diseases,which significantly impact the yield and quality of the crop.The management and prevention of these diseases depend mainly on their early identification and accurate classification.The central objective of this research is to propose and examine the application of Deep Convolutional Neural Networks(CNNs)as a potential solution for the precise detection and categorization of diseases impacting the leaves of South Indian mango trees.Our study collected a rich dataset of leaf images representing different disease classes,including Anthracnose,Powdery Mildew,and Leaf Blight.To maintain image quality and consistency,pre-processing techniques were employed.We then used a customized deep CNN architecture to analyze the accuracy of South Indian mango leaf disease detection and classification.This proposed CNN model was trained and evaluated using our collected dataset.The customized deep CNN model demonstrated high performance in experiments,achieving an impressive 93.34%classification accuracy.This result outperformed traditional CNN algorithms,indicating the potential of customized deep CNN as a dependable tool for disease diagnosis.Our proposed model showed superior accuracy and computational efficiency performance compared to other basic CNN models.Our research underscores the practical benefits of customized deep CNNs for automated leaf disease detection and classification in South Indian mango trees.These findings support deep CNN as a valuable tool for real-time interventions and improving crop management practices,thereby mitigating the issues currently facing the South Indian mango industry.