The cowpea seed beetle, Callosobruchus maculatus (Fab.) (Coleoptera: Chrysomelidae), is one of the most destructive pests of stored legumes worldwide, causing significant losses in seed weight, nutritional quality, and germination capacity. Increasing concerns regarding insecticide residues and environmental risks have intensified interest in plant-based alternatives for stored-product pest management. In the present study, the insecticidal potential of grape seed oil (Vitis vinifera L.) and black cumin oil (Nigella sativa L.) against adults of C. maculatus was evaluated under laboratory conditions (27 ± 2 °C and 65 ± 5
The antagonistic activity of Trichoderma harzianum against the soil-borne pathogens i.e. Fusarium oxysporum and Alternaria solani was evaluated in vitro using a dual culture assay. In addition, the effectiveness of a root bio stimulant containing humic acid, seaweed extracts (5
Global wheat production and food security have been majorly affected by drought stress. The research was carried out in order to assess 300 wheat genotypes under well-watered and drought conditions across two cropping seasons (2019–20, 2020–21). Split-split-plot layout with alpha-lattice design was utilized to design experiment with year considered as the main plot, water treatment (drought and control) was taken as subplot and sub-subplot factors were the genotypes with two replications. Several morphological traits were documented, which include plant height (PH), number of grains per spike (GPS), hundred grain weight (HGW), spike length (SL), number of spikelets per spike (SPS), number of spikes per plant (SPP) and yield per plant (YPP). Significant variation was observed among genotypes with strong genotype × treatment interaction for yield and its components. Drought stress was associated with a reduction in YPP by 29.3–44.0
Nanomaterials play a vital role in advancing sustainable agriculture. Recognizing the role of nanomaterials in sustainable growth and educating stakeholders on their benefits is essential. Nanomaterials play a vital role in all aspects of agriculture including machinery, agronomic practices and sustainability. It can improve efficiency, reduce resource use and promote sustainable operations. With advances in nanotechnology, biosensors are now used for monitoring crop parameters and early detection of plant stress. Nano-fertilizers and nano-pesticides are replacing conventional ones due to higher efficiency, effectiveness, and reduced environmental harm. They enhance crop yield compared to traditional fertilizers and control pests through nanopesticides. Nanomaterials are applied to machinery parts to reduce wear and withstand stress. Surfaces can be electroplated with nanomaterials to improve abrasion resistance. Nano additives in hydraulic, brake, transmission and engine oils reduce friction and maintain optimal thickness for smooth machinery operation. Nanomaterials are key to enhancing efficiency and sustainability in agriculture.
Accurate and timely identification of foliar diseases in sorghum (Sorghum bicolor L.) is essential for minimizing crop losses and ensuring food security. Manual scouting by agricultural experts remains the predominant diagnostic method, yet this approach is time-intensive, subjective, and unsuitable for continuous monitoring of large cultivation areas. Although deep learning has demonstrated considerable promise for plant disease classification, most existing models are developed using laboratory-captured or meticulously curated image datasets that inadequately represent the visual complexity, environmental variability, and imaging artifacts characteristic of operational farm settings. This paper presents LCDNet, a lightweight deep convolutional neural network architecture explicitly designed for real-time sorghum leaf disease classification under natural field conditions. The proposed six-layer architecture minimizes computational overhead while preserving strong feature extraction capabilities. LCDNet contains 171,459 trainable parameters and achieves an average CPU inference time of 73.31 ms per image, corresponding to 13.64 frames per second (FPS), demonstrating its potential suitability for rapid disease classification on resource-constrained platforms. A dataset comprising 930 high-resolution images of Anthracnose, Bacterial Streak, and healthy sorghum leaves was acquired directly from agricultural fields in Punjab, India, with expert verification of all labels. LCDNet achieves 98.83
