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Tomato leaf disease detection has been a challenge for farmers due to the need for quick and accurate diagnosis to prevent plant contamination. Traditional approaches such as visual observation and laboratory testing have limitations such as subjectivity and reliance on human expertise. Deep Learning (DL) approaches, particularly Convolutional Neural Networks (CNN), have emerged as a solution for disease identification and classification in plants. This research proposes a new variant of an inception-based sequential network that combines the advantages of deep residual and dense networks to reduce dimensionality and enhance accuracy, information flow, and gradient. Modified input image characteristics and hyper-parameters were used to classify existing networks on the tomato dataset, with experimental findings showing that the proposed model can achieve an average identification accuracy of up to 96 percent on the tomato test dataset. The model's performance surpasses most existing leaf identification models and requires fewer computation.

Tomato leaf disease identification by modified inception based sequential convolution neural networks