A Novel Deep Learning Models for Efficient Insect Pest Detection and Recommending an Organic Pesticide for Smart Farming
Keywords:
Deep learning; pest detection; organic pesticide; smart farmingAbstract
Plant pests pose a significant threat to agricultural production worldwide, as their outbreaks become increasingly intense and widespread. However, traditional methods of identifying these pests through lesion image segmentation are both inefficient and time-consuming, impeding the ability to generalize and apply their findings. To address this issue, this study introduces an enhanced convolutional neural network with Adaptive Particle Swarm Optimization with Long Short Term Memory (ICNN-APSO-LSTM), which improves the identification of plant pests in natural agricultural environments. The resulting pest identification system classifies harmful pests, enabling farmers to take corrective action. The study begins with an overview of current pest identification techniques, highlighting their pros and cons. Based on the limitations of these methods, the study proposes a new and improved classification technique. The mathematical model is derived using an objective function, combining pest recognition and pesticide recommendation using machine vision and CNN. The model also uses soil NPK sensors to acquire soil nutrient values, analyzing them to prescribe appropriate fertilizers. Choosing the right fertilizer for soil and yield is crucial for farming, and this article describes a powerful technique for estimating soil nutrient content and recommending suitable fertilizers. The study successfully identified five pests - aphids, magnolias, leaves, leaf miners, and sables - with over 99% accuracy. Field results using this technique resulted in recommended pesticide application times within 10 seconds and fertilizer recommendations within 80 seconds.
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