Sugarcane Disease Classification Using Soft Computing Algorithms

Pests and viral plant diseases have proliferated in agriculture. Rats spread plant diseases. Hard to detect viral infections or insect infestations that impair plant growth and yield. It decreases crop size, quality, and marketability, incurring major economic losses. Strong, disease-resistant crops are needed for agriculture. Bacterial, fungal, and insect/pest diseases destroy 70% and 15% of crops. Above 85% of disease severity destroys crops. Early detection systems track pests and diseases. High-definition equipment is costly for rural farmers. Correct diagnosis permits quick infection severity and type assessment and control. In precision agriculture, neural networks and image processing eliminate human error. Sustainable agriculture and organic agricultural cultivation must start early. The proposed system classifies diseases and pests for plant safety. A healthy, fast-growing plantation is our goal. Early detection of viral diseases and insect infestations boosts crop productivity. Plant disease diagnostics focuses on leaves and buds. Stem and insect infections are covered. The suggested method classifies plant health and disease automatically. Peanut leaves develop Bacterial Blight from contagious illnesses like Cercospora Leaf Peanut. Boll Weevils, European Corn Borers, and Fall Army Worms attack cotton. The conditions are categorised. Image accuracy depends on lighting, resolution, location, and backdrop complexity. Various low-resolution pictures are enriched to better classification. Complex leaf intensity variations cause the system to misdiagnose illness or insect infestation. To avoid misclassification, the system segments the sample image using various methods to find lesions. Multilevel Segmentation (MS) improves Deep neural network feature extraction using approximate outlines. This method protects important pixels, eliminates unneeded pixels, and amplifies the lesion to control segmentation and reduce insufficient and excessive segmentation.

Abstract: Plant diseases are a major threat to crop yield and food security. Early detection and diagnosis are crucial to prevent the spread of disease and minimize crop losses. Plant diseases can have devastating effects on crop yields and food security. Disease detection is crucial for effective disease management. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNN), have shown promising results in disease detection. In this study, we propose a plant disease detection system using CNN and Arduino. The system involves capturing images of plant leaves using a camera module connected to an Arduino board. In recent years, deep learning techniques such as Convolutional Neural Networks (CNNs) have shown great promise in image-based disease detection. Additionally, the use of low-cost microcontrollers like Arduino can provide a costeffective solution for real-time disease detection in the field. This paper proposes a plant disease detection system that combines CNN and Arduino for early and accurate disease detection. The system utilizes a CNN model to classify plant disease images and an Arduino board to analyze the results and provide real-time feedback to the user. The proposed system is tested on a publicly available dataset of plant disease images, achieving high accuracy, and demonstrating its potential for practical use. The results show that the proposed system can provide an affordable and efficient solution for early plant disease detection, facilitating prompt action and reducing crop losses.

Plant diseases have a direct impact on the productivity of crops, and therefore the early detection of diseases is crucial.

Agricultural production is one of the main sources of income in most countries. Enormous losses will be incurred if agricultural product is disturbed by plant disease. The key to reduce losses in agricultural product output and quantity is early detection of plant diseases. A diseased plant usually reflecting its disease by showing symptoms on its leaves. A potato leaf disease classification technique by using image processing and artificial neural network method is proposed in this study. The method can be used to determine the potato leaf is either healthy or diseased. With the aid of this technique, farmers can save time and cost in their farming activities. The main goal of this study is to detect potato plant (Solanum tuberosum L.) disease using image processing techniques. The K-Means clustering algorithm is used to segment the disease in potato leaf image. The segmented features of potato leaf disease are then extracted by using Gray Level Co-occurrence Matrix (GLCM) and these features are then fed into ANN for classification. With the proposed system, classification accuracy obtained is 94%.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

Chapter 2 - Current status of plant diseases and food security

Agricultural development is a critical strategy for promoting prosperity and addressing the challenge of feeding nearly 10 billion people by 2050. Plant diseases can significantly impact food production, reducing both quantity and diversity. Therefore, early detection of plant diseases through automatic detection methods based on deep learning can improve food production quality and reduce economic losses. While previous models have been implemented for a single type of plant to ensure high accuracy, they require high-quality images for proper classification and are not effective with low-resolution images. To address these limitations, this paper proposes the use of pre-trained model based on convolutional neural networks (CNN) for plant disease detection. The focus is on fine-tuning the hyperparameters of popular pre-trained model such as EfficientNetV2S, to achieve higher accuracy in detecting plant diseases in lower resolution images, crowded and misleading backgrounds, shadows on leaves, different textures, and changes in brightness. The study utilized the Plant Diseases Dataset, which includes infected and uninfected crop leaves comprising 38 classes. In pursuit of improving the adaptability and robustness of our neural networks, we intentionally exposed them to a deliberately noisy training dataset. This strategic move followed the modification of the Plant Diseases Dataset, tailored to better suit the demands of our training process. Our objective was to enhance the network's ability to generalize effectively and perform robustly in real-world scenarios. This approach represents a critical step in our study's overarching goal of advancing plant disease detection, especially in challenging conditions, and underscores the importance of dataset optimization in deep learning applications.

Agriculture industry represents an important component of Indian economy. Early detection of crop disease by using some latest technologies helps farmers to provide security to their crops. There are many types of disease found in different plants but it is not easy for farmers to detect at early stage of their life which is lead to yield loss and indirectly a financial loss to farmers. It is very tricky work, also required lots of time and knowledge to identify the exact disease on plant at a very initial stage. Latest technologies are very helpful to succeed in controlling this issue and hence here in this manuscript will provide a proper resolution along with the disease detection. The image processing with convolutional neural network (CNN), K-means clustering, Support Vector Machine (SVM), Naive Bayes is useful. Here this manuscript provides a literature review on Machine Learning (ML) techniques used in agricultural sector, mainly concentrating on the classification, detection of disease. Plant disease classification includes several steps like dataset creation, load creation, preparing, segmentation, feature extraction, training classifier and classification. The principal objective of this study is to get the information of various work done in this field and the future scope of research in this area.

Melanoma is one of the most deadly types of skin diseases, but there is an opportunity to survive in case of an early diagnosis. Our paper offers a contribution to assist dermatologists and experts to save time and effort to diagnose and treat melanoma in its early stages. Segmentation is necessary to focus the system on the skin lesion only, so we apply the Active contours algorithm to isolate the lesion area from the rest of the image. The PH2 dataset contains many features. In our proposed system, we focus on extracting color features through the Fuzzy color histogram (FCH) technique. The SVM, K-NN, ANN and FFNN classifiers present promising results, with 100% accuracy, 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> sensitivity and 100% specificity.

Deep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment. Owing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a non-invasive method for early detection of eye diseases. We used a hybridized CNN with deep learning (DL) based on two separate CNN blocks, to identify multiple Optic Disc Cupping, Diabetic Retinopathy, Media Haze, and Healthy images. We used the RFMiD dataset, which contains various categories of fundus images representing different eye diseases. Data augmenting, resizing, coping, and one-hot encoding are used among other preprocessing techniques to improve the performance of the proposed model. Color fundus images have been analyzed by CNNs to extract relevant features. Two CCN models that extract deep features are trained in parallel. To obtain more noticeable features, the gathered features are further fused utilizing the Canonical Correlation Analysis fusion approach. To assess the effectiveness, we employed eight classification algorithms: Gradient boosting, support vector machines, voting ensemble, medium KNN, Naive Bayes, COARSE- KNN, random forest, and fine KNN. With the greatest accuracy of 93.39%, the ensemble learning performed better than the other algorithms. The accuracy rates suggest that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. It contributes to the field of eye disease detection through the analysis of color fundus images by providing a reliable and efficient diagnostic system.

A review on automated plant disease detection: motivation, limitations, challenges, and recent advancements for future research

Plant diseases are a vital risk to crop yield and early detection of plant diseases remains a complex task for the farmers due to the similar appearance in color, shape, and texture. In this work, authors have proposed an automatic plant disease detection technique using deep ensemble neural networks (DENN). Transfer learning is employed to fine-tune the pre-trained models. Data augmentation techniques include image enhancement, rotation, scaling, and translation are applied to overcome overfitting. This paper presents a detailed taxonomy on the performance of different pre-trained neural networks and presents the performance of a weighted ensemble of those models relevant to plant leaf disease detection. Further, the performance of the proposed work is evaluated on publicly available plant village dataset, which comprises of 38 classes collected from 14 crops. The performance of DENN outperform state-of-the-art pre-trained models such as ResNet 50 & 101, InceptionV3, DenseNet 121 & 201, MobileNetV3, and NasNet. Performance evaluation of the proposed model demonstrates that effective in categorizing various types of plant diseases that comparatively outperform pre-trained models.

Plant diseases are one of the major causes of crop yield reduction worldwide. Early detection and effective management of plant diseases will ensure a sustainable food supply and reduce famine. In this project, we propose a Plant Disease Management System (PDMS) that uses Artificial Intelligence (AI) and Internet of Things (IoT) technologies to diagnose and manage plant diseases. The proposed system consists of two main components: a plant disease diagnosis module and a disease management module. The plant disease diagnosis module uses AI algorithms to analyze images of plant leaves and identify the type of disease affecting the plant. The AI model is trained on a large dataset of plant images using deep learning techniques, such as Convolution Neural Networks (CNNs) and Transfer Learning.

Infectious diseases of plants provide a substantial danger to the world's food supply and the agricultural industry. The early detection and classification of diseases that affect plant leaves is essential for minimizing crop loss and creating disease management measures that are both efficient and effective. For the purpose of this research, we present a unique approach that utilizes advanced machine learning techniques in order to classify plant diseases in a manner that is both accurate and efficient. In the first part of this multi-part series, we will begin by providing a detailed analysis of several different machine learning techniques, such as deep learning, convolutional neural networks (CNNs), and K-nearest neighbor (KNN), support vector machines (SVMs). Next, we provide an overview of a methodology for preprocessing the leaf images, which includes the addition of enhancements to the images, segmentation of the images, and the extraction of features. Next, we apply various machine learning algorithms to a large, diverse dataset of plant-leaf images that have varying degrees of disease severity and compare the performance of these algorithms as they are implemented on the dataset. Our findings provide evidence that the method being proposed is successful in correctly recognizing and categorizing plant diseases that affect leaf tissue. In terms of accuracy, precision, and recall, the models that are based on deep learning, in particular CNNs, perform significantly better than classical machine learning techniques. In addition, we investigate various methods to enhance the interpretability of the model and provide insights into the primary factors that contribute to the accuracy of categorization.

En este artículo planteamos una revisión de la literatura en diferentes bases de datos (CINAHL, CUIDEN, MEDLINE) con el fin de valorar la importancia de la neoplasia testicular en la adolescencia y el papel fundamental del profesional de enfermería, concretamente de la enfermera escolar, en la detección precoz de esta patología. Una vez analizado el tema y tras tomar conciencia de la importancia de enseñar prácticas de autoexploración testicular correctas, se ha concluido que la enfermera escolar no debe desarrollar únicamente una actividad asistencial, sino que entre sus funciones se debe contemplar la promoción de la salud y la prevención del cáncer testicular. Dentro del proceso educador, y como fomento de hábitos higiénicos en el adolescente, planteamos dadas las características de presentación de la neoplasia testicular la realización de talleres en los que se instruya a los jóvenes en la autoexploración testicular y la necesidad de realizar una técnica correcta, con el fin de fomentar la adquisición de hábitos de autoexploración testicular al igual que se hace en las mujeres para detectar precozmente la patología mamaria