Performance of deep learning vs machine learning in plant leaf disease detection

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Machine Learning Applications for Precision Agriculture: A Comprehensive Review

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

https://www.mdpi.com/2079-9292/10/12/1388/pdf

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Do we really need deep CNN for plant diseases identification

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

Deep feature based rice leaf disease identification using support vector machine

Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey

Nitrogen (N) concentration is a significant parameter to check the status of health in rice crop. Nitrogen (N) plays an essential role in the growth and productivity of rice plant. This paper proposes a convolutional neural network (CNN) based approach for prediction of rice nitrogen deficiency. The pre-trained CNN architecture is modified to improve the classification accuracy with the inclusion of pre-eminent classifier like support vector machine (SVM) by replacing the last output layer of CNN. Here, six leading deep learning architectures such as ResNet-18, ResNet-50, GoogleNet, AlexNet, VGG-16 and VGG-19 with SVM are used for prediction of nitrogen deficiency with 5790 number image samples. The performance of each classifier is measured and compared in terms of accuracy, sensitivity, specificity, false positive rate (FPR) and F1 score. Again, the statistical analysis is performed to choose the better classification model considering the results of 100 independent simulations. The statistical analysis confirmed the superiority of ResNet-50+SVM than the other five CNN-based classification models with an accuracy of 99.84%. Besides, the accuracy score of CNN classification models is compared with other traditional image classification models such as bag-of-feature, colour feature + SVM, local binary patterns (LBP) + SVM, histogram of oriented gradients (HOG)+SVM and Gray Level Co-occurrence Matrix (GLCM)+SVM.

Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network

The network reliability optimization problem for an interconnection network is to maximize the network reliability subjected to some constraints such as the total cost of the network. Even though, the problem is NP-Hard, many researchers have solved this problem in different ways but with a common assumption that nodes are perfect. But, this assumption is quite unrealistic in nature. In this paper, a new method based on artificial neural network is proposed to solve the network reliability optimization problem considering both the nodes and links of the interconnection networks to be imperfect. The problem is mapped onto an artificial neural network by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network reliability problem. Some existing methods are studied and compared with proposed method in evaluating the network reliability of some fully connected networks. The comparison reports the proposed method to be better than its counterparts in maximizing the network reliability. The proposed method is used to maximize the reliability of few fully connected networks subjected to some predefined total cost, where the node as well as the links of the networks may fail. Further, the behaviors of the cost as well as the time on the network reliability are discussed.

Network reliability optimization problem of interconnection network under node-edge failure model

This study was conducted to develop a prototype which computes the severity of diseases appears in the rice crop using machine learning and computational intelligence. The symptoms of rice crop diseases imply the seriousness of the disease and suggest choosing the best approach to dealing with the disease. Most of the diseases in rice crop appear as a spot on the leaves. It is also needful to diagnose the disease properly and on-time to avoid the great harm of the rice crop. The treatment of rice crop diseases by applying disproportionate pesticides increases the cost and environmental pollution. So the use of pesticides must be minimized. This can be actualizing by targeting the diseased area, with the appropriate quantity and concentration of pesticide by estimating disease severity. This paper introduces Fuzzy Logic with K-Means segmentation technique to compute the degree of disease severity of leaves in rice crop. The proposed method estimated to give up to about 86.35% of accuracy.

Nalini Kanta Barpanda

Papers

Deep feature based rice leaf disease identification using support vector machine

Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey

Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network

Network reliability optimization problem of interconnection network under node-edge failure model

Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence