Technical evaluation demonstrates high diagnostic accuracy in agricultural plant disease detection, highlighting opportunities to reduce crop losses for farmers.
To improve the usability and efficiency of agricultural disease detection, moving to a mobile application built with Flutter and Firebase integration is a promising solution. With the help of Convolutional Neural Networks (CNN) and MobileNet, along with TensorFlow & OpenCV in Machine Learning (ML) libraries, this mobile app can now identify plant diseases more quickly and with better accuracy. With an average accuracy of 98.12 percent, this solution not only provides farmers with a practical tool for early detection, but also allows them to take timely action toreduce crop losses. Also, its format is mobile app- friendly, meaning farmers can use the disease detection system at any point, anywhere, with their smartphones. This move to mobile is a significant step forward in using technology to effectively combat agricultural diseases, ultimately ensuring crop health and promoting national and economic stability.
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Arulmurugan et al. (2024) studied this question.
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