Advancing Crop Protection through Convolutional Neural Networks: A Multi-Plant Disease Classification Study
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Keywords:
Plant Disease Classifications, Convolutional Neural Networks (CNNs), Densenet169, Precision Agriculture, Food Security, Disease DiagnosisAbstract
Agriculture plays a pivotal role in sustaining human life by serving as the primary source of
food, yet its vulnerability to diseases poses a significant threat to global food security. The effective
management of these diseases is paramount to ensuring crop productivity and safeguarding the stability of
food systems worldwide. This study introduces a novel approach based on convolutional neural networks
(CNNs), leveraging the robust DenseNet169 architecture, for disease classification across four distinct plant
species: potato (Solanum tuberosum), tomato (Solanum lycopersicum), grapes (Vitis vinifera), and apple
(Malus domestica).The classification task encompasses identifying late blight, early blight, and healthy
states for potato and tomato; black rot, leaf blight, and healthy states for grapes; and apple scab, black rot,
and healthy states for apple. Remarkably, the proposed model demonstrates exceptional performance,
achieving an impressive accuracy rate of 99.5% on the classification task. This significant outcome
underscores the transformative potential of deep learning techniques in revolutionizing precision
agriculture practices. By automating disease diagnosis through advanced machine learning algorithms, such
as CNNs, this study pioneers a paradigm shifts in agricultural management. The implementation of
automated disease detection enables timely interventions, mitigating crop losses and enhancing overall
agricultural sustainability. This research not only showcases the efficacy of deep learning methodologies
but also underscores their instrumental role in addressing critical challenges faced by the agricultural sector.
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