Nutrient Deficiency Detection using a MobileNet: An AI-based Solution

Crop yield remains a pivotal determinant in the field of agriculture, susceptible to the adverse effects inflicted by various plant diseases during growth. The attainment of desired yields hinges upon the provision of adequate nourishment to plants. Nonetheless, insufficiencies in vital nutrients such as iron, magnesium, calcium, nitrogen, and potassium can significantly curtail production output. The identification and mitigation of these deficiencies require a multitude of machine learning methodologies such as artificial neural networks, convolutional neural networks, InceptionResNet, Densenet, VGG, and MobileNet. Among these techniques, MobileNet has demonstrated unparalleled accuracy, achieving an impressive 98% precision. DenseNet and InceptionResNet also exhibit formidable accuracies, registering at 96% and 94%, respectively. To facilitate a comprehensive comparison of these methods, the depiction of outcomes through graphical representations and diagrams proves instrumental in providing lucid visual comprehension. The MobileNet methodology entails the utilization of captured plant images as input, where the model undergoes training and testing to ascertain the presence of nutrient deficiencies or the plant's healthy state. This approach harnesses depthwise and pointwise convolutions, culminating in streamlined processing and optimal functionality. By harnessing the capabilities of MobileNet, the farmers are empowered to accurately detect and address nutrient inadequacies in crops, consequently leading to amplified yield and overall plant vitality.