The ability to accurately identify tomato leaves in a field setting is crucial for achieving early yield estimation, particularly with the growing importance of Precision Agriculture. It may be difficult to determine exactly what diseases are affecting tomato plants due to the overlap in symptoms between different diseases. These are the earliest signs of disease that we found in the leaves of tomato plants. Yellow leaf curl virus, leaf mold, light blight, early blight, Mosaic virus, Septoria leaf spot, and bacterial spot are just some of the seven types of plant leaf diseases that were taken into account in this paper. For the development of a testbed environment for data acquisition, the greenhouse at the university was utilized for data on the leaves of tomato plants. This study proposes a target detection model based on the improved YOLOv7 to accurately detect and categorize tomato leaves in the field. To improve the model’s feature extraction capabilities, we first incorporate the detection mechanisms SimAM and DAiAM into the framework of the baseline YOLOv7 network. To reduce the amount of information lost during the down sampling process, the max-pooling convolution (MPConv) structure is then improved. After that, this model arrived at a satisfactory outcome. Then, the image is segmented using the SIFT technique for classification, and the key regions are extracted for use in calculating feature values. After that, these data points are sent to a CNN classifier, which has a 98.8% accuracy rate and a 1.2% error rate. Finally, we compare our study to previous research to show how useful the proposed work is and to provide backing for the concept.

A critical advancement in solar photovoltaic (PV) establishment has led to robust acceleration towards the evolution of new MPPT techniques. The sun-oriented PV framework has a non-linear characteristic in varying climatic conditions, which considerably impact the PV framework yield. Furthermore, the partial shading condition (PSC) causes major problems, such as a drop in the output power yield and multiple peaks in the P–V attribute. Hence, following the global maximum power point (GMPP) under PSC is a demanding problem. Subsequently, different maximum power point tracking (MPPT) strategies have been utilized to improve the yield of a PV framework. However, the disarray lies in choosing the best MPPT technique from the wide algorithms for a particular purpose. Each algorithm has its benefits and drawbacks. Hence, there is a fundamental need for an appropriate audit of the MPPT strategies from time to time. This article presents new works done in the global power point tracking (GMPPT) algorithm field under the PSCs. It sums up different MPPT strategies alongside their working principle, mathematical representation, and flow charts. Moreover, tables depicted in this study briefly organize the significant attributes of algorithms. This work will serve as a reference for sorting an MPPT technique while designing PV systems.

This paper presents the Archimedes optimization algorithm to eliminate selective harmonics in a cascaded H-bridge (CHB) multilevel inverter (MLI). The foremost objective of the selective harmonic elimination (SHE) is to eliminate lower order harmonics by finding the optimal switching angle combination which minimizes the objective function containing Total Harmonic Distortion (THD) and other specific harmonic terms. Consequently, the THD is also reduced. In this study, a recently proposed metaheuristic technique named the Archimedes optimization algorithm (AOA) is used to determine the optimal angles corresponding to the 5, 7 and 9 level CHB-MLI. AOA involves equations related to a physical law, the Archimedes Principle. It is based on the idea of a buoyant force acting upward on a body or object that is partially or completely submerged in a fluid, and the upward force is related to the weight of the fluid displaced. This optimization technique has been implemented on CHB-MLI to generate various level outputs, simulated on MATLAB™ R2021a version environment software. The simulation results reveal that AOA is a high-performance optimization technique in terms of convergence speed and exploitation-exploration balance and is well-suited to the solution of the SHE problem. Furthermore, the laboratory validated the simulation result on a hardware setup using DSP-TMS320F28379D.

This paper used an artificial jellyfish search (AJFS) optimizer suitable for selective harmonic elimination-based modulation for multilevel inverter (MLI) voltage control application. The main objective was to remove the undesired lower-order harmonics in the output voltage waveform of an MLI. This algorithm was motivated by the behavior of jellyfish in the ocean. Jellyfish have the ability to find the global best position where a large quantity of nutritious food is available. The paper applied AJFS algorithm on five, seven, and nine levels of CHB-MLI. The optimum switching angle was calculated for the entire modulation range for the desired lower-order harmonics elimination. The problem formulated to achieve the objective was solved in a MATLAB environment. The total harmonic distortion (THD) values of five-, seven-, and nine-level inverters for various modulation indexes were computed using AJFS and compared with the powerful differential evolution (DE) algorithm. The comparison of THD results clearly demonstrated superior THD in the output of CHB-MLI of the AJFS algorithm over DE and GA algorithm for low and medium values of modulation index. The experimental results further validated the better performance of the AJFS algorithm.