Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

<span lang="EN-US">A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system.</span>

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A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/161052021 ◽

2021 ◽

Vol 10 (5) ◽

pp. 3037-3045

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Detection System ◽

Plant Diseases ◽

Healthy Plant ◽

Disease Detection ◽

Test Accuracy ◽

Plant Leaves ◽

Leaf Disease ◽

Deep Learning Network

Plants are seen as vital because they provide mankind with energy. Plant diseases can harm the leaf at any time between planting and harvesting, resulting in enormous losses in crop output and market value. A leaf disease detection system acts asignificant role in agricultural production. A large amount of labour is required for this process as well as an in-depth understanding of plant diseases. Determining the presence of illnesses in plant leaves requires the use of deep learning and machine learning methods, which classify the data based on a specified set. In this paper, apple and tomato leaves disease detection process is carried out by Chaotic Salp Swarm algorithm (CSSA) followed by Bi-directional Long Short Term Memory (Bi-LSTM) technique for classification. We've used the Bi-LSTM architecture to sense disease in tomato and apple leaves in studies. In order to determine the type of leaves, we trained a deep learning network using the PlantVillage dataset of damaged and healthy plant leaves. It is estimated that the trained model achieves a test accuracy of 96%.

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

Microprocessors and Microsystems ◽

10.1016/j.micpro.2020.103615 ◽

2021 ◽

Vol 80 ◽

pp. 103615

Author(s):

R. Sujatha ◽

Jyotir Moy Chatterjee ◽

NZ Jhanjhi ◽

Sarfraz Nawaz Brohi

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Disease Detection ◽

Plant Leaf ◽

Leaf Disease

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Deep Learning in Plant Diseases Detection for Agricultural Crops

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2020040103 ◽

2020 ◽

Vol 11 (2) ◽

pp. 41-58 ◽

Cited By ~ 6

Author(s):

Mohamed Loey ◽

Ahmed ElSawy ◽

Mohamed Afify

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Plant Disease ◽

Plant Diseases ◽

Food Plants ◽

Disease Detection ◽

Human Society ◽

Research Papers ◽

Limited Scope

Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

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