Application of Deep Learning Technique for Tomato Maturity Stage Prediction

Abstract

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural
applications, such as harvesting, grading, and quality control. Primary objective to develop an automated system
capable of identifying various defects in tomatoes and providing relevant treatment suggestions. Leveraging deep
learning techniques, a CNN model is trained to classify tomatoes into four categories: Damaged, Old, Ripen, and
Unripen. The implementation involves training a convolutional neural network and testing dataset of tomato images,
for classification, and deploying model intended real-time predictions. Project has potential to improve effectiveness of
tomato harvesting & reduce waste. The CNN model then predicts the stage of the tomato, providing the probability of
the prediction. Alongside the classification result, the system offers detailed information on the cause of the defect,
appropriate treatment methods, and nutritional content, aiding users in making informed decisions regarding the
tomatoes usability. The implementation ensures a robust and efficient detection mechanism, even in varying lighting
conditions and backgrounds. An Accuracy of 94.48% was attained when taught sculpt was used to make prediction on
the test dataset.