Efficient and precise application of pesticides in agricultural settings is crucial to ensure effective pest control while minimizing environmental impacts. The use of backpack magnetic sprayers has gained attention as a potential solution for improving spray deposition uniformity and reducing off-target drift. This study aimed to assess the performance of a backpack magnetic sprayer in a sugarcane plantation, specifically focusing on off-target drift and on-target deposition uniformity. Field experiments were conducted to evaluate the spray distribution pattern, droplet size spectrum, and deposition uniformity on target surfaces. The results were compared with a conventional backpack sprayer. The findings indicated that the magnetic sprayer significantly reduced off-target drift by enhancing droplet retention and deposition on the target surface. Moreover, it exhibited improved deposition uniformity compared to the conventional sprayer. The study provides valuable insights into the effectiveness of backpack magnetic sprayers in sugarcane plantations, highlighting their potential to optimize pesticide application and mitigate environmental risks

This study aims to analyze the hydraulic performance characteristics of an impact sprinkler equipped with a fixed water dispersion device. The performance of the sprinkler is evaluated based on parameters such as water distribution uniformity, throw radius, precipitation rate, and overall irrigation efficiency. Experimental tests are conducted to measure these parameters under various operating conditions, including different water pressures and nozzle sizes. The results provide valuable insights into the sprinkler's hydraulic behavior and its ability to deliver water effectively and efficiently in agricultural and landscape irrigation systems. The findings of this research can aid in optimizing the design and operation of impact sprinklers with fixed water dispersion devices, leading to improved irrigation practices and water resource management.

Accurate and efficient assessment of plant photosynthetic pigment contents is crucial for monitoring plant health, growth, and stress responses. Traditional methods for pigment analysis require time-consuming laboratory procedures and specialized equipment, limiting their practicality for real-time monitoring in the field. In this study, we present a novel approach that utilizes artificial intelligence (AI) techniques within a mobile application for real-time assessment of plant photosynthetic pigment contents. The application integrates image analysis algorithms based on deep learning models to analyze plant leaf images captured by a mobile device's camera. The AI model accurately identifies and quantifies various photosynthetic pigments, including chlorophylls and carotenoids, providing instant information about plant physiological status. Experimental evaluations demonstrated the application's robustness and accuracy in estimating pigment contents across different plant species and growth stages. This mobile-based AI approach offers a convenient and rapid tool for on-site monitoring of plant health and can facilitate precision agriculture practices.

Saffron is a valuable spice derived from the stigma of Crocus sativus flowers. The separation of saffron stigmas from petals is a critical step in the processing of saffron, as it directly affects the quality and purity of the final product. In this study, a computational fluid dynamic (CFD)-based numerical simulation of a cyclone separator is performed to optimize the separation efficiency of saffron stigmas from petals. The cyclone separator is modeled and simulated using CFD techniques to analyze the flow patterns and particle trajectories within the separator. The simulation results provide insights into the design and operating parameters that enhance the separation efficiency. The optimized cyclone separator design can contribute to improved saffron processing techniques and ensure the production of high-quality saffron products.