Abstract
Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.
Highlights
Plant diseases lead to the loss of modern farming production
For training and testing of the convolutional neural network, all the images used in this study were resized to 224 × 224, 227 × 227, 227 × 227 and 300 × 300 pixels by IrfanView software (Version 5.50, Irfan Skijan, Jajce, Bosnia) for VGG-16, AlexNet, SqueezeNet and EfficientNet-B3 models respectively according to the network input size for more accessible learning, validation and testing processes
The performance of trained Convolution Neural Network (CNN) models (AlexNet, VGG-16, SqueezeNet, EfficientNet-B3) for the classification of leaf scorch diseased infected plants were evaluated under two different lighting situations during the image acquisition in the field experiments (Figure 4)
Summary
Plant diseases lead to the loss of modern farming production. The emergence of plant diseases has a negative impact on agricultural crop yield. Deep convolution neural networks have been extensively used in the farming sector for example in weed, pest, and disease identification, fruit, flowers and plant classification for yield assessment, and in autonomous vehicles for navigation purposes [33,34,35]. Convolution Neural Network (CNN) is one of the most popular machine learning (ML) methods, according to published reports for the classification of crop diseases. Sladojevic et al [41] suggested a different method to distinguish 13 diverse plant diseases by deep convolutional neural networks Another author reported a powerful deep learning-originated device for real-time use that can identify nine various diseases of tomato plants [42]. Multicore GPUs have increased the speed of learning on very large image data
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