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Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy. [ABSTRACT FROM AUTHOR]

Recent advancements in wireless sensor networks (WSNs) and precision agriculture have captured significant interest in both research and industry. Integrating WSNs into agriculture offers numerous benefits, including enhanced crop quality, improved production efficiency, resource conservation (water and energy), and optimized crop management practices (such as irrigation and fertilization). Irrigation, a crucial factor for agricultural success, is often hampered by current systems’ inefficiencies and rising production demands. Water scarcity, drought, and limited water resource waste further complicate conventional irrigation. To address these issues, we propose an intelligent irrigation system prototype that combines WSN technology with drip irrigation. This system utilizes WSNs and ZigBee wireless communication to create an automated and intelligent irrigation solution that conserves water, enhances irrigation system performance, and improves efficiency. Sensor data collected from strategically placed sensors near plant roots (including temperature and soil moisture) is used to control irrigation. The system analyses this data against predefined thresholds for each parameter and activates or deactivates irrigation accordingly. This approach aims to achieve significant water, time, and energy savings.

Crop protective agents (CPAs) are prime requisite for protecting crops from pests and diseases. CPAs are used at different stages of the crop from the seed sowing to harvesting. Seeds are treated with different fungicides as a prophylactic measure against pre-emergence diseases and during seed germination stage different protective agents are applied to safeguard young seedlings from pests. To find out the effect of crop protective agents on seed germination and seedling growth in chilli (Capsicum annum L.) an in-vitro study was conducted using Ethion (1.25, 2.5, 3.75 and 5.0mL/L), Triazophos (0.625, 1.25, 1.875 and 2.5mL/L), Flubendiamide (0.15, 0.3, 0.45 and 0.6mL/L), Emamectin Benzoate (0.2, 0.4, 0.6 and 0.8 g/L) and Tebuconazole + Trifloxystrobin (0.625, 1.25, 1.875 and 2.5 g/L) against control (distilled water). Correlation study revealed that seedling growth was positively affected by Flubendiamide (r = 0.287), neutral by Emamectin Benzoate (r = 0.012), while retarded growth was observed in Ethion (r = -0.584), Trizophos (r = -0.473) and Tebuconazole+ Trifloxystrobin (r = -0.331) chemicals compared to control. From the statistical analysis, it was found that chilli seed germination was not affected at the tested concentration levels of crop protective agents. Seedling shoot length was significantly (P = 0.05) reduced in Tebuconazole + Trifloxystrobin (1.763cm) and Ethion (3.175cm) compared to control (5.088cm). Seedling shoot length was not significantly affected by Emamectin Benzoate and Triazophos, while it was significantly increased in Flubendiamide@0.45mL/L (5.88cm) compared to control (5.088cm). Seedlings in Flubendiamide and Emamectin Benzoate produced significantly (P=0.05) longer roots of 7.488cm and 7.688cm respectively, while seedlings in Ethion, Tebuconazole + Trifloxystrobin and Triazophos produced shorter roots (4.00, 4.45 and 4.6cm respectively) compared to control (6.363cm). Rotten seed per cent on 15th day was significantly (P=0.05) lower in Tebuconazole + Trifloxystrobin and Triazophos (5.61 and 11.73%) compared to control (22.45%).

This paper presents the simulation of a 15-level cross-H bridge multilevel inverter using control techniques like sinusoidal pulse width modulation and third harmonic injection pulse width modulation. This paper aims to enhance output voltage level using fewer switches and to decrease total harmonic distortion by improving overall efficiency. Compared to other inverter topologies cross H bridge topology uses a lesser number of switches and dc voltages. Analysis and simulation of 15-level cross H bridge MLI are performed and presented.

Present days renewable energy sources based grids are developing which needs inverters to convert power from DC to AC. 2-level inverters were used for this purpose to overcome the drawbacks of 2-level inverters multilevel inverters are developed. Because of the combination of numerous devices in a series structure, multilevel inverters provide outstanding solutions to high voltage, high power applications. With the use of suitable simulations and mathematical analysis, this paper compares the performance of 19 and 21-level cascaded H-Bridges with cross H-Bridge MLI. Total Harmonic Distortion levels, switching device count, and inverter output voltage and current are all compared. The primary issues brought up in this study are limitations of a number of switching devices that can support high voltage in the inverter. The benefit of this research is that it identifies the right inverter that can be utilised for real-time applications by taking the variables into account. The count of switching devices, output voltage, current, and harmonic distortion, among other factors. The Matlab/Simulink Platform is used to validate the analysis.