Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks

The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

Section: Classificationmentioning

confidence: 99%

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

et al. 2024

Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models

John,

Meva,

Arora

2024

Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate the occurrence of vehicular accidents, our research endeavors revolve around the comprehensive analysis of the prevailing road conditions. This meticulous examination allows us to effectively automate traffic routes orchestration, ensuring smooth vehicular movement across all lanes of the road network. The analysis of traffic patterns is conducted through the utilization of visual data images. The real time captured traffic images undergo processing using various object detection models named RetinaNet and the YOLO (You Only Look Once) models. A series of comparative evaluations suggests an improved traffic object identification capacity for the RetinaNet model as compared to all YOLO models.

Predictive Modeling of Crop Yield Using Deep Learning Based Transformer with Climate Change Effects

Garg,

Arora

et al. 2024

Climate change is a significant global challenge concerning agriculture and food security. The understanding of climate change effects on crop production is necessary for developing an effective adaptation strategies and predicting a crop yield accurately. This paper suggests the combined Clustering Long Short Term Memory Transformer (CLSTMT) model for crop yield prediction. CLSTMT is a hybrid model that integrates clustering, deep learning based LSTM and Transformer techniques. The outliers from the historical crop and climate data are removed using k-means clustering. Followed by, the crop yield is predicted using Transformer-based neural network with LSTM layers and feed-forward neural network (FNN) components. The model design effectively captures climate-influenced patterns, enhances the precision and comprehensiveness of crop yield prediction. The experiment is conducted using the dataset with crop yield, climate, and pesticide details over 101 countries collected from 1990 to 2013. The comparative analysis reveals that the CLSTMT model outperforms other regression models such as SGDRegressor (SGDR), Lasso Regression (LR), Support Vector Regression (SVR), ElasticNet (EN) and Ridge Regression (RR). The proposed design effectively captures climate-influenced patterns, enhancing the precision and comprehensiveness of crop yield predictions. The findings indicate that the proposed model provides an accurate prediction of crop yield with high R2 of 0.951 and lesser Mean Absolute Percentage Error (MAPE) of 0.195. This value suggests a minimal average percentage deviation between the actual and predicted yields. The findings indicate that the CLSTMT model provides more accurate crop yield prediction compared to others.