Innovative Approaches to Enhance Sericulture: Integrating Drone Technology and Artificial Intelligence for Precision Cultivation of Mulberry Fields in Bangladesh

The research was conducted in the Rajshahi and Jessore districts, which are key sericulture regions in Bangladesh. According to the Bangladesh Sericulture Development Board's (BSDB) 2022 Annual Report, these areas encompass approximately 900 hectares of mulberry cultivation, primarily managed by smallholder farmers [7]. The climate in these regions is suitable for mulberry cultivation, with distinct wet and dry seasons that influence agricultural practices.

The DJI Agras MG-1P is an advanced agricultural drone designed to enhance efficiency and productivity in farming operations. This type of Drone were used for pesticide application and airborne surveillance. Because these drones have GPS and high-resolution cameras, they can precisely monitor and manage mulberry plantations. The drones were designed to fly along predetermined routes over the fields, gathering information on insect infestations and crop health as well as precise picture and data capture.

TensorFlow is an open-source machine learning framework developed by the Google Brain team. It is widely used for building and deploying machine learning models across a variety of platforms, including desktops, mobile devices, and the cloud. Tensor Flow was used to create custom AI algorithms that analyzed drone surveillance data. The AI system was trained to recognize important mulberry health indicators, including soil moisture content, pest presence, and leaf chlorophyll levels. Convolutional neural networks (CNNs) were employed by the system for image recognition and analysis, which made it possible to accurately spot abnormalities and possible problems in the fields.

Data were collected over two cropping seasons (July-October 2022 and March-June 2023). Parameters such as leaf yield, pest incidence, and resource usage (water and fertilizers) were meticulously recorded. The data were analyzed using Statistical Package for Social Sciences (SPSS) software to compare the outcomes of traditional sericulture practices with those of the technology-integrated approach. Statistical methods, including t-tests and Analysis of Variance (ANOVA), were employed to assess the significance of the differences observed.

The implementation of drone technology and AI in mulberry field management resulted in a significant increase in leaf yield. According to data, traditional farming practices result in an average of 2500 kg/ha of leaf production, setting the minimum productivity that can be achieved through established agricultural ways without technology incorporation. In contrast, the average yield of 2950 kg/ha in technological integrated farming methods indicates an approximately 18% increase in productivity. This significant improvement is because present day agricultural technologies such as precision farming, automated irrigation systems and data driven decision making tools have made it possible to farm precisely (Table 1).

These technologies align resource use efficiencies and optimize growing conditions leading to increased crop yields. The results however demonstrate the potential advantages that could accrue from embracing technology-integrated farming approaches; these raise productivity and profitability for farmers while also promoting sustainable agriculture. Technology integrated approaches can thus help alleviate the food shortage through efficient resource utilization and improved crop yields besides minimizing the ecological impacts associated with farming activities as global food demand rises every year. This increase is primarily attributed to precise pest management and optimized resource use, which ensured healthier and more productive mulberry plants [7].

The AI system demonstrated high accuracy in identifying pest-infested areas, allowing for targeted pesticide application. This precision reduced overall pesticide use by 28%, minimizing environmental impact and reducing costs for farmers (Table 2 & Figure 1). The reduction in pesticide uses also contributed to the health of beneficial insect populations and the broader ecosystem [7].

By using artificial intelligence to analyse soil moisture and nutrient content, water and fertilizer usage were optimized. Figure 2 shows a comparison between traditional and technology integrated farming methods in terms of water and fertilizer use. This table highlights the great reductions that have been made as a result of the application of advanced agricultural technologies. In terms of water usage, traditional farming methods need 5000 liters per hectare while technology-integrated ones bring it down to 3900 liters per hectare, implying a decrease by 22% (Figure 3). This fall is largely accounted for by highly accurate and efficient irrigation systems that are designed to deliver water exactly where it is needed most and minimize losses.

Similarly, traditional methods employ 200 kilograms of fertilizers per hectare whereas technology-integrated methods reduce this quantity to 176 kilograms per hectare making it possible to save up to 12% (Figure 3).

For Bangladesh's agriculture to be sustainable over the long run, these improvements reduced costs and encouraged sustainable farming methods [7]. In order to achieve these optimizations, it is imperative that big data and machine learning technologies be integrated, as discussed in a research article that addressed how data-driven insights might result in more effective resource management in agriculture [1], [2].

For farmers, the result of the higher leaf yield and lower resource consumption was enhanced profitability. For the farmers who took part, the cost savings from using less fertilizer and pesticides, along with their enhanced output, significantly raised their net income. Comparing modern methods to traditional ones, farmers saw an average 25% improvement in profit margins [7].

The results of this study show that productivity and sustainability in sericulture can be greatly increased by integrating drone technology and artificial intelligence. Farmers benefit from enhanced profitability due to lower resource utilization and increased leaf yield. As described by Wolfert et al. (2017) and Liakos et al. (2018), the use of big data and machine learning in agriculture offers a strong basis for comprehending how these technologies might be used to enhance sericulture practices in Bangladesh [1], [2]. Farmers may accomplish better results with fewer resources by utilizing data-driven insights and precision farming practices, which will guarantee a more lucrative and sustainable future for the sericulture sector.

Notwithstanding the encouraging outcomes, a number of obstacles stand in the way of these technologies' broad adoption. Smallholder farmers, who sometimes have little capital, may find it difficult to make the initial investment needed for drones and AI systems. Technical instruction and continuing upkeep are also required to guarantee the efficient usage of these technologies. To make these technologies available to all farmers, government subsidies, low-interest loans, and training initiatives should be part of the solution to these problems.

The complexities of sericulture pose unique challenges compared to other agricultural sectors [8]. One of the main technical challenges is ensuring the reliability and accuracy of AI algorithms in diverse field conditions. Variations in weather, light, and soil conditions can affect the performance of AI systems. Continuous improvements in algorithm robustness and adaptability are necessary to address these issues.

Social and cultural issues frequently lead to reluctance towards the adoption of new technologies. Farmers who have been using conventional ways for many generations may be reluctant to adopt new technologies. It will take community involvement and educational initiatives to break down these obstacles and show off the advantages of technology integration.