


Issue release: 30 June 2025
This study investigates pesticide residues in cucumber plants and their impact on soil nematode populations while evaluating the effect of pesticides on cucumber growth and yield. Gas Chromatography Tandem Mass Spectrometry (GC-MS/MS) was used to quantify pesticide residues, comparing the results to the Maximum Residue Limits (MRLs) defined by the Codex Alimentarius. Significant differences in residue levels were found between various pesticides and application rates. Diazinon residues ranged from 0.86 to 2.28 mg/kg, exceeding the MRL of 0.1 mg/kg, indicating soil contamination. Endosulfan had the lowest residues, from 0.44 to 1.75 mg/kg, which were within acceptable limits. Conversely, Malathion and Methoxychlor residues notably surpassed their MRLs, raising potential safety concerns. Further analysis using a linear regression model revealed a negative correlation between pesticide application and soil nematode populations. There was a proportional decrease in nematode populations with increasing pesticide application rate, with Malathion having the most significant impact, followed by Endosulfan, Methoxychlor, and Diazinon. The impact of pesticide application on cucumber plant growth and yield was assessed using one-way ANOVA, which uncovered significant differences across treatment groups. While pesticides are effective for pest control, their application must be carefully managed to avoid phytotoxicity and ensure optimal plant and environmental health, thereby enhancing maximum productivity.

Prof. Zhengjun Qiu
Zhejiang University, China

Cheng Sun
Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China
Indexing & Archiving
In the realm of modern agriculture, the integration of cutting-edge technologies is revolutionizing the way we approach sustainable farming practices. A recent study published in Advances in Modern Agriculture titled "Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery" has garnered significant attention for its innovative approach to precision irrigation management. Conducted by researchers from Institute of Data Science and the AgriLife Research and Extension Center of Texas A&M University (authors's information is below). This study introduces a novel method for classifying cotton water stress using unmanned aerial vehicles (UAVs) and convolutional neural networks (CNNs), offering a powerful solution for optimizing water use in agriculture.
Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.
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