Plant Disease Detection using Ensembled CNN Framework

Abstract

Agriculture exhibits the prime driving force for growth of agro-based economies globally. In the field of agriculture, detecting and preventing crops from attacks of pests is the major concern in today's world. Early detection of plant disease becomes necessary to prevent the degradation in the yield of crop production. In this paper, we propose an ensemble based Convolutional Neural Network (CNN) architecture that detects plant disease from the images of the leaves of the plant. The proposed architecture takes into account CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). The approach helped us in building a generalized model for disease detection with an accuracy of 97.9 % under test conditions.

Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring

“…Recognizing the importance of this problem, agricultural sector researchers have achieved substantial advancements, particularly in Deep Learning applications, with convolutional neural networks (CNNs) leading these developments. Known for their exceptional ability to process complex visual data, CNNs excel in a variety of intricate tasks such as object recognition, image classification, and instance segmentation, all of which are highly valuable in numerous agricultural applications (e.g., [2][3][4][5][6][7][8][9]). These applications underscore the versatility and adaptability of CNNs in meeting the diverse challenges faced by the agricultural sector.…”