The study was conducted at Mundopasha, Wazirpur, Barisal during Rabi season of 2017 for testing, adoption and popularization of different conservation machinery (CA) such as zero till planter (ZT), strip till planter (ST), bed planter (BP) and power tiller operated seeder (PTOS) along with conventional tilling and of sowing method for planting of mungbean (BARI Mung-6). The soil type was loamy-sand with bulk density 1.41 g/cc. The effective field capacities of ZT, ST, BP, PTOS and power tiller were found to be 0.104, 0.109, 0.084, 0.109, and 0.074 ha/h, respectively. The field efficiency of ZT, ST, BP, PTOS and power tiller were estimated as 74.68, 76.47, 75.84, 78.16 and 71.52%, respectively. Significantly the highest mungbean grain yields were found from ZT and ST planted plots than those of other plots. Significantly the lowest grain yield was obtained from conventional tillage and broadcasting method. The highest benefit cost ratio (BCR) was obtained from ST planted (2.60) and zero till planted (2.40) mungbean followed by PTOS (2.20), conventional tillage cum broadcasting (1.88) and bed planting (1.82) methods. The lowest BCR was found from traditional tillage and manual line sowing method (1.61) of mungbean. CA planting system saved about 50% planting cost and reduced about 76% carbon dioxide emission. Based on the fuel consumption, grain yield and BCR, ZT planter and the strip till planter may be recommended in Barisal region for cultivation of mungbean. Show less

Sorghum is important staple food for feeding humans and animals in sub-Saharan African country. Nowadays, with the advent of climate change couple to the population growth, the crop is faced to new challenges wish are gradually affecting the productivity. In Niger, a dry land country where farmers are growing sorghum at a small scale for family subsistence, the crop is confronted to biotic and abioticonstraints including nutrient deficiency in the soil. Among those constraint, Striga is the one that causes huge damage to sorghum cultivation through yield reduction. In addition, nutrient depletion in the soil are causing weed progresses in farmer’s field at a large scale. In fact, to overcome Striga problem several control methods have been successfully tested, but some of them are efficient but not accessible to farmers. It is important to control the weed by using effective and accessible way for smallholder farme in Niger. The main objective of this study was to assess the effectiveness of different doses of sesame (0, 0.5 and 1.5 g) and DAP (0, 2 and 5 g) on Striga impact and sorghum grain yield. Two (2) grammes micro dosing utilization of DAP shows good result in Striga plant emergency and distribution along sorghum field. Concerning the three doses of sesame seed involve in this experiment, the micro dosing two (1.5 g) positively affect Striga effect. The combination sesame seed and DAP can significantly reduce Striga impact on sorghum cultivation and increased sorghum grain yield. Show less

Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems. Show less