In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.

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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks

Deep learning has gained widespread adoption in various fields, including object recognition, classification, and precision agriculture. This study aimed to investigate the use of deep convolutional neural networks for the real-time identification of diseases in tomato plant leaves. A customized field dataset was constructed, consisting of several images of tomato leaves captured using a mobile phone from agricultural fields in the Kerala and Tamil Nadu regions and classified into two categories: healthy and diseased. A YOLO v5 deep learning model was trained to classify images of tomato leaves into the respective categories. This study aimed to determine the most effective hyperparameters for the classification and detection of healthy and sick leaves sections, using both proprietary and publicly available datasets. The YOLO v5 model demonstrated a notable accuracy rate of 93% when evaluated in the test dataset. This method can help farmers quickly recognize diseased leaves and prompt the implementation of preventive measures to curtail the spread of tomato plant diseases.

An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset

In today’s world, agriculture is one of the most important assets as it affects the economy of the country. Biotic stress caused by fungi, bacteria, and viruses is common in plants; hence plant disease recognition is essential as it could identify diseases at the early stages of infection. Most of the previous plant disease works involve PlantVillage dataset, which is taken in a controlled environment. In this paper, the Banana leaf dataset is used which is combined from two datasets. CNNs are becoming more popular in many forms, such as pre-trained models, and in this research, we employed a pre-trained model in conjunction with an ML classifier to predict plant disease. Pre-trained models such as VGG 16, RESNET, MobileNet, InceptionResNetV2, Inception, and Xception models were deployed for feature extraction. Based on the above models, the proposed Xception-RF model achieved 100% accuracy with no misclassified classes. Using an Xception model with depth-wise convolutions reduces the number of parameters and the cost of implementation. The proposed work merges machine learning and deep learning by extracting features using CNN and then using ML-based RF for classification, which has resulted in an increase in performance in terms of accuracy. 

An Hybrid Feature Extraction and Classification using Xception-RF for Multiclass Disease Classification in Plant Leaves

Modified Xception-RF: A biotic stress prediction model in plant leaves using feature extraction and classification

The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artificial intelligence algorithms. In this study, a comprehensive collection of over 10,000 high-definition images was amassed, showcasing a wide variety of herbaceous plants afflicted by prevalent diseases such as leaf rust, blight, and powdery mildew. These images underwent a series of feature extraction processes to underscore the disease signatures. We employed and assessed several machine learning frameworks, particularly Convolutional Neural Networks and Support Vector Machines With regard to their precision and operational efficacy. The outcomes suggested that the CNN model excelled, achieving an impressive 95% accuracy rate in pinpointing diseases, thereby surpassing conventional diagnostic approaches. It not only provides a powerful solution for swift and precise disease detection in herbaceous plants but also paves the way for future advancements in automated plant health surveillance systems. The findings help farming methodologies, suggesting a pathway towards enhanced disease control and minimization of crop loss. 

Herbaceous Plant Leaf Disease Identification

Agronomy is viewed to be the mainstay of every country and it is important for the economy of all the nations worldwide. As an agricultural country, the nation’s food supply relies upon a sufficient supply of healthy plants, hence identification of plant health is very important. Loss in productivity of crop occurs with the pathogen, pest, weed, and animals. Also, lack of water content and other factors could lead to deficiency in plants. The population of the country increases rapidly day by day, hence the productivity of the crop must increase. Therefore, the identification of various deficiencies, diseases, and various techniques used by both machine learning(ML), deep learning(DL), Artificial Neural Network(ANN) techniques is studied and highlighted. Besides, this detailed review will help identify the gaps and enable us to find a better solution for the betterment of plant health. This paper presents an introduction to plant health and its various stress and a analysis of works on the applications of deep DL, ML, ANN concerning various plant health issues, identification, classification, and prediction.

Review on Plant health and Stress with various AI techniques and Big data

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Review on Plant health and Stress with various AI techniques and Big data