Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%.

We present a novel variation of the vehicle routing problem (VRP). Single commodity cargo with pickup and delivery service is considered. Customers are labeled as either cargo sink or cargo source, depending on their pickup or delivery demand. This problem is called a single commodity vehicle routing problem with pickup and delivery service (1‐VRPPD). 1‐VRPPD deals with multiple vehicles and is the same as the single‐commodity traveling salesman problem (1‐PDTSP) when the number of vehicles is equal to 1. Since 1‐VRPPD specializes VRP, it is ???????? hard in the strong sense. Iterative modified simulated annealing (IMSA) is presented along with greedy random‐based initial solution algorithm. IMSA provides a good approximation to the global optimum in a large search space. Experiment is done for the instances with different number of customers and their demands. With respect to average values of IMSA execution times, proposed method is appropriate for practical applications.

Deep neural networks (DNNs) have recently been applied in many areas of agriculture, including pest monitoring. The codling moth is the most damaging apple pest, and the currently available methods for its monitoring are outdated and time-consuming. Therefore, the aim of this study was to develop an automatic monitoring system for codling moth based on DNNs. The system consists of a smart trap and an analytical model. The smart trap enables data processing on-site and does not send the whole image to the user but only the detection results. Therefore, it does not consume much energy and is suitable for rural areas. For model development, a dataset of 430 sticky pad photos of codling moth was collected in three apple orchards. The photos were labelled, resulting in 8142 annotations of codling moths, 5458 of other insects, and 8177 of other objects. The results were statistically evaluated using the confusion matrix, and the developed model showed an accuracy > of 99% in detecting codling moths. This developed system contributes to automatic pest monitoring and sustainable apple production.

The pear leaf blister moth is a significant pest in apple orchards. It causes damage to apple leaves by forming circular mines. Its control depends on monitoring two events: the flight of the first generation and the development of mines up to 2 mm in size. Therefore, the aim of this study was to develop two models using artificial neural networks (ANNs) and two monitoring devices with cameras for the early detection of L. malifoliella (Pest Monitoring Device) and its mines on apple leaves (Vegetation Monitoring Device). To train the ANNs, 400 photos were collected and processed. There were 4700 annotations of L. malifoliella and 1880 annotations of mines. The results were processed using a confusion matrix. The accuracy of the model for the Pest Monitoring Device (camera in trap) was more than 98%, while the accuracy of the model for the Vegetation Monitoring Device (camera for damage) was more than 94%, all other parameters of the model were also satisfactory. The use of this comprehensive system allows reliable monitoring of pests and their damage in real-time, leading to targeted pest control, reduction in pesticide residues, and a lower ecological footprint. Furthermore, it could be adopted for monitoring other Lepidopteran pests in crop production.