Bilingual Mobile Plant Disease Diagnostic System through Offline Deep Learning Inference

Identifying the detection of plant diseases remains a chronic menace in contemporary farm settings, with farmers unable to manage the economic downturns caused by the lack of resources and long periods of waiting to get expert agronomic advice. Current diagnostic methods are highly biased towards in depth visual examination by trained staff as well as laboratory pathology analysis thus posing a bottleneck in terms of lengthy turnaround times, high cost and impractical situation in real agricultural practice. This paper outlines a smartphone-based, bilingual diagnostic tool, which combines the concept of computational simplified deep learning systems with user-friendly interface structure and speech functionality. The developed tool uses a Convolutional Neural Network redesigned as a TensorFlow Lite system, which allows conducting analysis computations on a handheld device without the constant need to have an internet connection. The images of the leaves that are taken with the cameras of mobile devices are processed and evaluated with the taxonomic indicators to determine the indicators of plant wellness or identify particular forms of illness. The tool inserts crop-targeted classification systems and alters the classification outcome, involved on the basis of the specified botanical specimens. With reference to the accessibility requirements, the instrument has a dual-language feature, which can operate in English and Tamil, in addition to speech synthesis features, which audio-visually details the diagnostic conclusions to the end users. The architecture has been modified to maintain the stability of operations and consistency of the user experience over repeated interactions by including verification processes, historical analysis archiving, and capabilities of preserving data in disconnected modes. The tool separates the impermissible and appropriate photographic contributions, a non-infected situation on the plants, and pathology, consequently minimizing the instances of erroneous evaluation. Combining image-based analytical algorithms, cross-language inter-user interaction, as well as audio-based assists to make decisions, this mobile tool will be a grounded and farmer-centered technological solution. The model with a validation accuracy of 93.04 in the training phase and the deployment model with a validation accuracy of 92.27 and a mean inference time of 28.28 m/s validates the use of the model in real-time smartphone-based agriculture. The deployment strategy attests to the feasibility of deploying the state-of-the-art agricultural diagnostic equipment on cost-efficient mobile devices, progressing fast disease-detection schedules, minimizing the undue use of chemicals, and enhancing agricultural practices that are sustainable to the environment.