Domain-Adaptive TinyML Model for Efficient Pest and Disease Detection in Domestic Crops: A Practical Approach for Developing Countries

Artificial intelligence (AI) is a disruptive technology that has achieved remarkable breakthroughs across various domains and is reshaping our daily lives. However, AI's resource-intensive, time-consuming, and domain-specific nature creates a significant divide between developed countries, with abundant resources, and developing countries, where computing power and datasets are limited. To bridge this gap, Tiny Machine Learning (TinyML), Transfer Learning (TL), and Domain Adaptation (DA) emerge as crucial approaches for implementing AI in resource-scarce environments. TinyML deploys compressed AI models on resource-constrained devices, including mobile devices and microcontrollers. TL leverages knowledge from one task to enhance performance on a related task, while DA, a sub-discipline of TL, applies models trained on one distribution to a related but different distribution. This paper presents a feasibility study on applying DA to develop a TinyML model for detecting and classifying diseases in domestic crops. We proposed a 2D-CNN model named "AgroNet," equipped with a domain classification head to predict the input domain, thus enhancing adaptability to different agricultural domains. We evaluated its performance with a TL-based 2D-CNN model and a traditional 2D-CNN baseline model in both source and target domains. The source domain dataset encompassed tea, apples, coffee, tomatoes, beans, and maize, while the target domain dataset included peas, cabbage, cassava, cowpea, and spinach-evaluation metrics comprised precision, f-measure, and accuracy. Results indicated superior performance in the source domain compared to the target domain. However, AgroNet exhibited satisfactory performance in the target domain, achieving an average score of over 0.7 across all metrics, while the baseline and TL-based models displayed significant performance drops, recording averages of less than 0.5 in most classes. The TL and baseline models' reliance on static features limited their adaptability, particularly in the target domain characterized by diverse image characteristics. These findings underscore the necessity of extensive retraining for traditional approaches in new domains. Regarding memory and energy requirements, AgroNet consumed more resources than the other ML models, with 200MB and 500 joules expended over 61 seconds of training. We deployed the AgroNet model in an apple farm in Kericho County embedded in a mobile device. The energy and memory requirements were sufficient for execution on resource-scarce devices, as evidenced by the mobile devices, including embedded systems. The model recorded average results during real deployment with a significant drop in classification performance compared to simulation environments, likely due to noise introduced in the deployment environment, including background noise and differing lighting conditions. This study demonstrated the feasibility of DA for AI deployment in resource-constrained settings, as it can be trained on a source domain and applied in a comparable target domain after limited fine-tuning. This saves on computing costs and accelerates deployments. In the future, we will explore ensemble learning techniques, including boosting and bagging, and feature engineering methods, such as principal component analysis, feature selection, and data augmentation, to enhance machine learning classification performance.