Single and multi-crop species disease detection using ITSO based gated recurrent multi-attention neural network

Abstract

<p>Diseases of crop plants pose a serious danger to agricultural output and progress. Predicting the onset of a disease outbreak in advance can help public health officials better manage the pandemic. Precision agriculture (PA) applications rely heavily on current information and communication technologies (ICTs) for their contribution to long-term sustainability. Preventative measures against plant diseases require accurate early disease prediction in order to be effective. The current computer vision-based illness detection technology can only detect the disease after it has already manifested. This research intends to provide a deep learning (DL) method for early disease attack prediction using Internet of Things (IoT) directly sensed environmental factors from crop fields. There is a robust relationship between environmental factors and the life cycles of plant diseases. Disease incidence in plants can be forecast based on environmental variables in the crop field. In order to solve these issues, the research presented here suggests using a gated recurrent multi-attention neural network (GRMA-Net). The study uses multilevel modules to zero down on informative areas in order to extract additional discriminative features, as informative characteristics tend to appear at various levels in a network. In order to capture long-range dependence and contextual interaction, these characteristics are first organised as spatial sequences and then input into a deep-gated recurrent unit (GRU). Finally, an enhanced version of the Tunicate swarm optimisation model (ITSO) is used to pick the best values for the model’s hyper-parameters. Four public datasets representing a wide range of crop types are used to assess the model’s efficacy. Some of these databases cover numerous crop species, like PlantVillage (38 categories), while others focus on a single crop, such as Apple (4), Maize (4), or Rice (5). The experimental findings show that the system achieves 99.16% accuracy in identifying agricultural diseases, which is higher than the accuracy of other current deep-learning approaches.</p>