Revolutionizing Agriculture with Deep-Tech: AgriSciense's Innovative Solution to Early stage Pest Detection

As the co-founder and CTO of AgriSciense, I am excited to share with you our journey in the deep-tech space, specifically addressing one of the most pressing challenges in agriculture: pest management. Alongside my esteemed co-founders Sudip and Joshua, we have embarked on a mission to revolutionize the way pests are detected and managed in farms, with a particular focus on combating the infestation of the red-palm weevil insect in date palm trees. The Problem: Early Detection of Pests in Agriculture

Pests pose a significant threat to agricultural productivity, leading to substantial crop losses and economic downturns for farmers worldwide. Identifying and mitigating pest infestations at an early stage is crucial to preventing widespread damage and ensuring sustainable yields. However, traditional methods of pest detection often rely on visual inspections, which can be time-consuming, labor-intensive, and prone to human error.

Our Approach: Innovative Hardware and Software Solutions

At AgriSciense, we are harnessing the power of deep-tech to develop cutting-edge hardware and software solutions that enable early detection of pests in tree farms. Central to our approach is our custom-made hardware device, equipped with highly sensitive sensors capable of detecting vibrations caused by tree borer insects, including the notorious red-palm weevil. Utilizing a metallic probe as a waveguide, our hardware sensor captures these vibrations and transmits them to a micro-controller, generating WAV format files.

Our Solution: Integrating TinyML for Real-time Pest Detection, predictive analytics Building upon our hardware foundation, we have implemented advanced machine learning techniques, particularly leveraging TinyML support within our micro-controller. We have developed an Audio Classification ML model trained to analyze the WAV files generated by our hardware and accurately predict whether a tree is infested with pests. By detecting subtle patterns and anomalies in the audio data, our model can provide real-time insights into pest activity, enabling farmers to take proactive measures to mitigate infestations swiftly. Our Current Stage: Moving from Concept to Reality Currently, we are in the final stages of developing our proof of concept for the hardware component of our solution. With rigorous testing and refinement, we aim to ensure that our hardware device meets the stringent requirements for accuracy, reliability, and durability in field conditions. In the next two weeks, we will initiate pilot testing in date palm farms to evaluate the effectiveness of our technology in real-world environments.

Furthermore, our ML model has undergone extensive testing using public datasets, achieving an impressive accuracy rate of approximately 85%. However, we remain committed to continuous improvement and refinement, leveraging feedback from our pilot testing phase to enhance the performance and robustness of our solution further.

In conclusion, AgriSciense is poised to revolutionize agriculture with our innovative approach to pest detection. By combining state-of-the-art hardware with advanced machine learning capabilities, we aim to empower farmers with the tools and insights needed to safeguard their crops and optimize yields sustainably. As we embark on this transformative journey, we invite you to join us in shaping the future of agriculture through deep-tech innovation. Together, we can create a more resilient and prosperous agricultural sector for generations to come.