Wheat crop classification and prediction are important tasks for the optimization of crop yield and resource utilization. In this study, we propose an Artificial Neural Network (ANN) model integrated with Genetic Algorithm (GA) to predict and classify the wheat crop images of different ages. The dataset of 19,300 images was used, and the model was trained and tested using various performance evaluation metrics. The results show that the proposed ANN+GA model achieved the highest accuracy of 99.29% during the training phase and 98.65% during the testing phase. The model was also compared with other state-of-the-art machine learning models, and the proposed model was found to be superior in terms of accuracy, specificity, sensitivity, precision, and F-measure. The graph of training and testing accurateness and loss values in contradiction ofindividual epoch demonstrating the speediness of model convergence. Our proposed model is feasible and robust, giving better classification and crop forecast outcomes for numerous wheat crop age groups with least resource necessities. These findings could be useful for farmers and agricultural researchers in improving crop yield and resource management.

An innovative smart agriculture system utilizing a deep neural network and embedded system to enhance crop yield

Detection and identification of disease in plants is the fundamental step to avoid various constraints and to enhance productivity. In agricultural farm, the crops are prone to various bacterial and fungal disease. It is initiated due to the varied ecological conditions and soil categories. The earlier prediction of diseases helps to prevent the complete degradation of crops. The disease is detected through small patches on the leaves of the plant. The evolution of automation and innovations helps in the earlier detection of disease in the leaves and fruits of the crops. It is achieved by artificial intelligence. This is done through the image processing techniques. It uses the comparison of healthy plant leaves to that of the infected leaves and fruits. The classification of disease in the fruits and leaves are done accurately through the convolution neural network. This helps to detect the type and cause of the bacterial and fungal infections. This includes image pre-processing, data acquisition and image classification with feature extraction. Thus the CNN model helps to detect and classify the disease in plant and fruits in agricultural farm.

Classification of Automatic Detection of Plant Disease in Leaves and Fruits using Image Processing with Convolutional Neural Network

An earlier diagnosis of cancer rises the living days of patients. The evolution of medical systems supports to identify the presence of cancer. In the state-of-the-art works, few methods were implemented for diagnosing breast cancer with different Deep Neural Network (DNN) support. However, the traditional DNN techniques were computationally expensive as it does provide better feature extraction performance with minimal time usage. Besides that, the misclassification error observed during the diagnosis process was higher, impacting the accuracy of early breast cancer prediction. Therefore, a novel Scale Invariant Deep Feature Learning Based Boosted Support Vector Entropy Classification (SIDFL-BSVEC) method is introduced. The SIDFL-BSVEC method is proposed to get better accuracy for the early identification of breast cancer with lesser time. The SIDFL-BSVEC method initially designs Scale Invariant Deep Neural Multiple Feature Extraction (SIDNMFE) algorithms to discover critical features in input mammograms with minimal time by working as robust to illumination variations, noise, partial occlusion, and minor viewpoint changes in mammograms images. These traits are significant for early cancer disease prediction when cells in the mammogram image have dissimilar sizes and orientations. After that, Support Vector Entropy Boosted Cancer Classifier (SVEBCC) algorithm is designed to minimize the error rate determined during the early analysis of breast cancer. The performance of the proposed CWDCMFE-MBRC technique is verified by taking the parameters such as disease diagnosis accuracy, disease diagnosis time, sensitivity, specificity and error rate along with various numbers of input images. 

Scale Invariant Deep Neural Multiple Feature Learning Based Boosted Support Vector Entropy Classification for Breast Cancer Diagnosis using Mammograms

Visual Place Recognition (VPR) has engrossed the interest of many researchers in several domains like robotics and Computer Vision (CV). It is the process of identifying a location visited previously, depending on visual input like videos or images. DL techniques have witnessed a potential to solve this task. One method for VPR utilizing deep learning is Convolutional Neural Networks (CNNs) to extract features in visual input. The CNN is trained on a large dataset of videos or images, with each video or image corresponding to a diverse location. The CNN extract discriminative features for each location, allowing it to detect previously visited locations depending on their visual appearance. Therefore, this study presents an Arithmetic Optimization Algorithm with Deep Learning-Driven Robust Visual Place Recognition (AOADL-VPR) technique. The AOADL-VPR technique's purpose is to recognise the visual places using the DL model properly. In the AOADL-VPR technique, Gaussian Filtering (GF) based pre-processing is performed to remove the noise. Meanwhile, the MobileNet-v2model is utilized for generating a feature vector set.Furthermore, the AOA is exploited to adjust the hyperparameter values of the MobileNet-v2 model.At last, Minkowski Distance is exploited for effectual similarity measurement between two images, thereby recognising the places.A series of experimental analyses can be performed to ensure the improved performance of the AOADL-VPR approach.The simulation outcomes portrayed the enhancements of the AOADL-VPR system on the place recognition process. 

Visual Place Recognition Model using Deep Learning with Arithmetic Optimization Algorithm

Plant diseases pose a significant risk to global food supply and finances. This research paper develops a system using EfficientNet-B3 to classify plant leaf diseases. The PlantVillage dataset, with 54,303 curated images, is utilized. The model accurately identifies plant leaf diseases, with extensive testing and comparisons. A user-friendly web platform allows convenient submission and classification of plant leaf photos, providing prompt and reliable results for disease identification. The research has implications for revolutionizing plant disease detection and control strategies. By utilizing deep learning and EfficientNet-B3, this system offers an effective method for early disease detection, enhancing agricultural practices, and improving global food security.

Enhancing Plant Leaf Disease Classification Performance Through EfficientNetB3 and Hyperparameter Optimization

Agriculture is one of the most crucial determinants of a country's development. Agriculture employs more than 60% of the population in India. Plant disease is a persistent problem for smallholder farmers, posing a threat to income and food security as the seasons change. These diseases attack the leaves of the plant initially, then infect the entire plant, reducing the quality and quantity of the crop produced. It is impossible for a human to recognize and diagnose each plant's status due to a large number of plants on the farm. Because these ailments are contagious, it's vital to identify each plant. The deep convolutional Neural Network model's accuracy results in disease identification revealed that it is promising and can have a major impact on disease identification efficiency.

Identification of Maize Plant Leaf Disease Detection using ConvNet Model

Breast cancer (BC) is the world’s most common and rapidly spreading disease. BC can be controlled, reducing the death rate if detected early. As a result, some researchers have recently developed deep-learning-based efficient algorithms for predicting cancer cell growth using different modalities of medical imaging, for example, mammograms, tomosynthesis, MRI ultrasound, etc., for their efficiency and accuracy. Only a few review articles on BC diagnosis synthesize some existing studies. These investigations, unfortunately, are unable to identify new structures and modalities in the diagnosis of BC. The changing frameworks of DL for BC detection are the subject of this review. This assessment explores the strengths and limitations of earlier deep-learning (DL) based methods, investigates the datasets employed, and examines image preprocessing approaches based on different medical imaging modalities. The performance results with evaluation, obstacles, and further enhancement are also provided. 

/pdf/a-deep-learning-based-methodological-analysis-for-breast-17eu9dj9.pdf

A Deep Learning Based Methodological Analysis for Breast Cancer Classification

This paper outlines a way for identifying plant leaf infections and a method for locating infections with caution. The goal of the proposed research is to use image processing and support vector machines to analyze leaf disease (SVM). The diseases on the leaf are the primary cause of the rapid fall in leaf development. Because 85–95 percent of diseases occur on the leaf, the approach used to identify leaf disease. This research involves K- means bunching, which is the division calculation, as well as the Support Vector Machine. This proposed recognition model based extremely successful in perceiving leaf illnesses. Record Terms k-implies, Leaf illnesses, picture division, characterization, Threshold, picture handling.

Image Segmentation Enhanced with Machine Learning Techniques for Automatic Detection of Plant Leaf Diseases

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Papers

Identification of Maize Plant Leaf Disease Detection using ConvNet Model

A Deep Learning Based Methodological Analysis for Breast Cancer Classification

Image Segmentation Enhanced with Machine Learning Techniques for Automatic Detection of Plant Leaf Diseases