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Since it satisfies all prerequisites for the growth of humanity, agriculture is currently regarded as being the most significant sector for civilization. One of the main forms of human energy production is thought to be plants, which also provide nutrients, cures, etc. Any damage or disease brought on by exposure to pathogens, viruses, bacteria, etc., while cultivating plants results in a decline in productivity, making it crucial to prevent such diseases and take the required precautions to avoid them. Accurately identifying such fatal diseases is a crucial first step for both the businesses and farmers. Six different Convolutional Neural Networks (CNNs) that accept plant leaf images as input, along with the Enhanced Symbiotic Organism Search (ESOS) optimization algorithm, have been implemented in our research. We intend to extensively contrast the various models based on accuracy, precision, recall, and F1-score. In the area of image recognition and classification, convolutional neural networks (CNNs), in particular, and deep learning, in general, are developing. The literature contains a variety of CNN designs. The dataset size, the number of classes, the model's weights, hypermeters, and optimizers are a few examples of the variables that have an impact on a CNN model's performance. Because of its benefits, transfer learning and fine-tuning a pre-trained model are now very popular. This study examines the impact of six popular CNN models: DenseNet, MobileNet, EfficientNet, VGG19, ResNet and Inception. As a result, DenseNet demonstrates an optimal accuracy rate of 98% when compared to other models. [ABSTRACT FROM AUTHOR]

In agriculture, selecting an "appropriate plant for an appropriate soil" is a crucial stage for all sorts of lands. There are different types of soil found in India. It is necessary to understand the features of the soil type to predict the types of crops cultivated in a particular soil. This leads to significant inconsistencies and errors in large-scale soil mapping. However, manually analyzing the soil type in the laboratory is cost-effective and time-consuming, yet it produces an inaccurate classification result. To overcome these challenges, a novel AQU-FRC Net (Aquila – Faster Regional Convolutional Neural Neural) is proposed for the automatic prediction of soil and recommending suitable crops based on a soil-crop relationship database. The soil images were pre-processed using a Scalable Range-based Adaptive Bilateral Filter (SCRAB) for eliminating the noise artifacts from the images. The pre-processed images were classified using Faster-RCNN, which utilized MobileNet as a feature extraction network. The classification results were optimized by the Aquila optimization (AQU) algorithm that normalizes the parameters of the network to achieve better results. The proposed AQU-FRC Net achieves a high accuracy of 98.16% for predicting soil. The experimental results demonstrate that the model successfully predicts the soil when compared to other meta-heuristic-based methods. [ABSTRACT FROM AUTHOR]

Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren't always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way. [ABSTRACT FROM AUTHOR]

Overfishing of marine fishery is a serious threat to fishery ecological security. Fishing supervision is one of the main ways to maintain marine fishery ecology. In order to improve the intelligence of fishing supervision system, a real time fish detection method based on YOLO-V3-Tiny-MobileNet was proposed. Aiming at the problems of shallow network layers and insufficient feature extraction ability in YOLO-V3-Tiny network, the proposed network takes YOLO-V3-Tiny as baseline and combines it with MobileNet. The proposed network is pre-trained by VOC2012 dataset, and then retrained and tested on Kaggle_ NCFM (The Nature Conservancy Fisheries Monitoring) dataset. The experimental results show that the proposed method has superior performance in parameters number, mean average precision and detection performance, compared with other methods. Compared with the monitoring method of fishing vessel detection on shore supervision, the real time monitoring method can give timely warning to the fishing vessel operators, which is more conducive to fishery ecological protection. [ABSTRACT FROM AUTHOR]

Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference. [ABSTRACT FROM AUTHOR]

Though deep learning networks have proven ability to perform video analytics in complex environments, there is an increased attention towards the development of compact networks which would facilitate edge processing and the result of which have yielded high performance compressed deep learning networks such as, MobileNet, PWCNet and BindsNet. In the work proposed herein, a dual network configuration is used for human action recognition, wherein, the MobileNet captures the spatial appearance of the action sequences and the PWCNet is used to extract the motion vectors. A novel Spiking Neural Network (SNN) based configuration is used as the classifier and the SNN implementation is based on BindsNet. The proposed configuration is experimentally validated on challenging datasets, viz., HMDB51 and UCF101. The experimental results demonstrate that the proposed work is superior to the state-of-the-art techniques and comparable in few cases. [ABSTRACT FROM AUTHOR]