Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data’s complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RL-block uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a real-life numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively.

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm (KNN). Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge.

The modern age is an era of fast‐growing technology, all thanks to the Internet of Things. The IoT becomes a prime factor of human life. As in this running world, no one cares about the wastage of food. However, this causes environment pollution as well as loss of many lives. A lot of researchers help in this era by introducing some great and beneficial projects. Our work is introducing a new approach by utilizing some low‐cost sensors. In this work, Arduino UNO is used as a microcontroller. We use the eNose system that comprises MQ4 and MQ135 to detect gas emission from different food items, i.e., meat, rice, rice and meat, and bread. We collect our data from these food items. The MQ4 sensor detects the CH₄ gas while the MQ135 sensor detects CO₂ and NH₃ in this system. We use a 5 kg strain gauge load cell sensor and HX711 A/D converter as a weight sensor to measure the weight of food being wasted. To ensure the accuracy and efficiency of our system, we first calibrate our sensors as per recommendations to run in the environment with the flow. We collect our data using cooked, uncooked, and rotten food items. To make this system a smart system, we use a machine learning algorithm to predict the food items on the basis of gas emission. The decision tree algorithm was used for training and testing purposes. We use 70 instances of each food item in the dataset. On the rule set, we implement this system working to measure the weight of food wastage and to predict the food item. The Arduino UNO board fetches the sensor data and sends it to the computer system for interpretation and analysis. Then, the machine learning algorithm works to predict the food item. At the end, we get our data of which food item is wasted in what amount in one day. We found 92.65% accuracy in our system. This system helps in reducing the amount of food wastage at home and restaurants as well by the daily report of food wastage in their computer system.

An early detection of tomato leaf diseases is crucial for ensuring high-quality and abundant crop production. There is a growing need for an Android application that can identify these diseases and alert farmers to take timely and effective measures, thereby reducing crop yield losses. While deep learning models have been instrumental in detecting plant leaf diseases, they often involve complex models and significant computational demands to achieve optimal performance. This study introduces a feasible lightweight model optimized for an Android application to detect diseased areas within images. This research focuses on enhancing the efficiency and accuracy of tomato leaf disease detection by modifying mobile-based Convolutional Neural Networks (CNNs). This model employs two parallel network streams based on the core principles of MobileNetv3, utilizing inverted residual blocks (IRBs) to improve accuracy at both low and high-level features, operating across different image dimensions. Additionally, inverted residual connections are incorporated to expand the model’s receptive field. To maximize feature utilization, cross-layer connections are introduced between the two parallel streams, integrating the Efficient Channel Attention (ECA) module to reduce the number of parameters. The model is trained using transfer learning with specific adjustments to minimize detection errors and is fine-tuned using a tomato leaf disease image dataset extracted from Plant Village. This feature integration and data analysis scheme are then deployed to provide a tomato leaf disease detection Android app. Evaluation of the model’s performance is conducted through correct predictions and quality metrics, with the average training accuracy for the ten classes found to be 98.77%. The research aims to assess the model’s effectiveness through comprehensive experiments, ultimately contributing to advancements in agricultural applications.

Retinitis pigmentosa (RP) is a group of genetic retinal disorders characterized by progressive vision loss, culminating in blindness. Identifying pigment signs (PS) linked with RP is crucial for monitoring and possibly slowing the disease’s degenerative course. However, the segmentation and detection of PS are challenging due to the difficulty of distinguishing between PS and blood vessels and the variability in size, shape, and color of PS. Recently, advances in deep learning techniques have shown impressive results in medical image analysis, especially in ophthalmology. This study presents an approach for classifying pigment marks in color fundus images of RP using a modified squeeze-and-excitation ResNet (SE-ResNet) architecture. This variant synergizes the efficiency of residual skip connections with the robust attention mechanism of the SE block to amplify feature representation. The SE-ResNet model was fine-tuned to determine the optimal layer configuration that balances performance metrics and computational costs. We trained the proposed model on the RIPS dataset, which comprises images from patients diagnosed at various RP stages. Experimental results confirm the efficacy of the proposed model in classifying different types of pigment signs associated with RP. The model yielded performance metrics, such as accuracy, sensitivity, specificity, and f-measure of 99.16%, 97.70%, 96.93%, 90.47%, 99.37%, 97.80%, 97.44%, and 90.60% on the testing set, based on GT1 & GT2 respectively. Given its performance, this model is an excellent candidate for integration into computer-aided diagnostic systems for RP, aiming to enhance patient care and vision-related healthcare services.

Visual saliency techniques based on Convolutional Neural Networks (CNNs) exhibit an excessive performance for saliency fixation in a scene, but it is harder to train a network in view of their complexity. The imparting Residual Network Model (ResNet) that is more capable to optimize features for predicting salient area in the form of saliency maps within the images. To get saliency maps, an amalgamated framework is presented that contains two streams of Residual Network Model (ResNet-50). Each stream of Reset-50 that is used to enhance the low level and high level semantics features and build a network of 99 layers at two different image scales for generating the normal saliency attention. This model is trained with transfer learning for initialization that is pre-trained on ImageNet for object detection and with some modification to minimize prediction error. At the end, the two streams integrate the features by fusion at low and high scale dimensions of images. This model is fine-tuned on four commonly used datasets and examines both qualitative and quantitative evaluation metrics with state of the art deep saliency models outcomes.