Abstract
In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, beans is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, beans are a great source of protein that offer many health benefits, but there are a lot of diseases associated with beans leaf which hinder its production such as angular leaf spot disease and bean rust disease. Thus, an accurate classification of bean leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify beans leaf disease by using public dataset of leaf image and MobileNet model with the open source library TensorFlow. In this study, we proposed a method to classify beans leaf disease and to find and describe the efficient network architecture (hyperparameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying bean leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using MobileNetV2 architecture under the some controlled conditions as MobileNet to check if we could get faster training times, higher accuracy and easier retraining, we evaluated and implemented MobileNet architectures on one public dataset including two unhealthy classes (angular leaf spot disease and bean rust disease) and one healthy class, the algorithm was tested on 1296 images of bean leaf. The obtained results showed that our MobileNet model achieves high classification performance for beans leaf disease, the classification average accuracy of the proposed model is more than 97% on training dataset and more than 92% on test data for two unhealthy classes and one healthy class.
Highlights
Beans is one of the most widely consumed seeds in the world and it is an important crop worldwide, 30% of the crop is produced by small farmers in Latin America and Africa [1], beans are an important source of protein that offer many health benefits, despite its significance, beans plants are susceptible to various diseases, some of these disease caused by the fungus organisms, while others are bacterial [2]
An automatic system needs to be developed to control this disease very early, identification of crop diseases using some automatic techniques is very useful as it decrease the work of supervision especially in big fields of production, one such technique is the automatic classification of bean leaf diseases using deep learning models, automatic classification of beans leaf diseases is an important research topic performed to provide benefits to the farmer as it is important in controlling large fields of crops and at a very early stage and very rapidly
There are other studies that used MobileNet model such as classification of tomato leaf diseases using MobileNet V2 (90% accuracy) presented by Siti Zulaikha et al in [20], Vinutha et al in [21] introduced Crop monitoring study using MobileNet models to identify 8 crop species and 15 labelled diseases based on training deep convolutional neural network, the trained model achieved an accuracy between 90% and 99% on the testing dataset
Summary
Beans is one of the most widely consumed seeds in the world and it is an important crop worldwide, 30% of the crop is produced by small farmers in Latin America and Africa [1], beans are an important source of protein that offer many health benefits, despite its significance, beans plants are susceptible to various diseases, some of these disease caused by the fungus organisms, while others are bacterial [2]. Due to the wide cultivation of beans crops, it is susceptible to diseases which in turn affects its production, this is the motivation that recognition of leaves unhealthiness is the solution for saving the beans crops and productivity, the objective of this work is to develop an automated model capable of classifying and identifying disease type based on MoblieNet, beans leaf images and based on an efficient network architecture in order to build an accurate models that can be classified bean leaf disease into their classes, we evaluated and compared MobileNet architectures using single public dataset and comparing their results for classification of bean leaf disease to find the best usable architecture and optimum classification results, in order to achieve this comparison of the effectiveness of different architectures, all the parameters have to be controlled under the same conditions using the same dataset. The rest of the paper organized as follows: A literature survey about the existing work were discussed in existing work section, discussion about the dataset and system configuration and training process were presented in research materials and methods section, the experimental setup and result discussion were presented in result and discussion section, and followed by conclusion
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