Diseased Portion Cassification & Recognition of Cotton Plants using Convolution Neural Networks

Abstract

Cotton plant is one of the cash crops in India. For more profit its intense care is necessary. Many researchers are using machine learning for early detections of cotton plant disease. Convolution neural network (CNN) is a deep feed forward artificial neural network. This algorithm is little faster as compared to other classification algorithms. In this paper, CNN is used for classification of the diseased portion of cotton plant images. The result shows that the model used classifies the healthy and diseased cotton leaves more accurately.

Cotton Leaf Disease Detection Using Instance Segmentation

“…Based on biological growth of cotton plants, it affects the collection of cotton leaf of different classes in same region. So in primary level healthy and unhealthy identification of leaf is done in this paper (Udawant, P. et al, 2019). Transfer learning technique can be used to identify the region of diseased portion.…”

Sustainable Smart Agriculture: Plant disease detection with deep learning techniques in cotton cultivation

“…For instance, Mikail and Baran (2021) used artificial neural networks and support vector machine AI methods to predict cotton production. Udawant and Srinath (2019) used CNN to classify the diseased part of cotton plant images with a high accuracy rate to differentiate between healthy and diseased cotton plant (Udawant and Srinath 2019). As part of a transfer learning methodology (2020), Nunes Alves et al ( 2020) proposed a classification system for cotton pests (primary and secondary), obtaining initial weights from ImageNet and using the proposed insect image dataset, in which original images as well as augmented images were used to train the networks (Nunes Alves et al 2020).…”