Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network

Abstract

Plant diseases are a severe cause of crop losses in the agriculture globally. Detection of diseases in plants is difficult and challenging due to the lack of expert knowledge. Deep learning-based models provide promising ways to identify plant diseases using leaf images. However, need of larger training sets, computational complexity, and overfitting, etc. are the major issues with these techniques that still need to be addressed. In this work, a convolutional neural network (CNN) is developed that consists of smaller number of layers leading to lower computational burden. Some augmentation techniques such as shift, shear, scaling, zoom, and flipping are applied to generate additional samples increasing the training set without actually capturing more images. The CNN model is trained for apple crop using a publicly available dataset PlantVillage to identify Scab, Black rot, and Cedar rust diseases in apple leaves. The rigorous experimental results revealed that the proposed model is well fit to identify apple leaf diseases and achieves 98% classification accuracy. It is also evident from the results that it needs lesser amount of storage and takes smaller execution time than several existing deep CNN models. Although, there exist several CNN models for crop disease detection with comparable accuracy, but the proposed model needs lower storage and computational resources. Therefore, it is highly suitable for deploying in handheld devices.

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

“…Ahmadet al, [24] have presented toward generalization of deep learning-based plant illness recognition using controlled and field situations. Using five datasets, the presented work assesses the performance of deep learning methods generalize to identify plant diseases in a various datasets and environmental conditions using five datasets.…”

A novel hierarchical framework for plant leaf disease detection using residual vision transformer

“…To identify the diseases in all apple leaf varieties, Krishan Kumar et al [ 21 ] developed a CNN model that produced an accuracy of 98 % after training it with 1000 epochs. A lightweight deep neural network was proposed by Sabbir Ahmed et al [ 22 ] by fusing a refined pre-trained model and a classifier network.…”

Smart Greenhouse and Warehouse Monitoring with Disease Detection using Machine Learning

“…The implementation of a security system inside a greenhouse holds significant importance in ensuring the protection, safety, and productivity of the greenhouse environment [12]. An IR sensor is used to detect the infrared radiations.…”