Recommendation of Farming Equipment Using Deep Learning

In order to maximize crop productivity and reduce resource waste, precision agriculture significantly depends on the effective use of agricultural equipment. However, because there are so many possibilities and farm circumstances vary so much, choosing the best farming equipment for a certain task can be difficult. In this paper, we provide a deep learning based recommendation system designed specifically for choosing precision agricultural equipment. The recommendation system makes use of a network of deep neural networks architecture that was trained on an extensive dataset that included a range of characteristics, including crop variety, soil type, field size, weather, and equipment performance in the past. Convolutional neural networks, or CNNs, are used by the model to extract temporal and spatial information, that is, from the input data. This enables the system to recognize intricate connections and patterns inside agricultural setting. In order to manage missing values, standardize features, and enhance the data for reliable model training, the dataset is preprocessed. The evaluation measures, which are determined using actual use cases and field testing, includes precision, recall, and F1-score. In addition, current streams of data and feedback from users for ongoing development are incorporated to assess the system’s scalability and flexibility. Significant obstacles to agricultural production, stability of the environment, and global food security are pre- sented by plant diseases. The purpose of this abstract is to give a brief introduction to plant diseases, including information on their origins, symptoms, effects on plant and agriculture, preventative and management techniques, and importance for ecosystem health and agriculture.