Abstract
A crucial but time-consuming duty in agricultural practices is spotting disease in any crop. Chillies are a vital income crop in Telangana whose cultivation is essential for preserving food security. However, a number of diseases and pests can cause significant productivity losses while growing chillies. Early detection of these diseases is crucial for efficient management and the reduction of yield losses. The goal of this study is to analyze best convolution neural network model that can recognize and categorize chilli plant illnesses with accuracy. In this chapter, the classification of the four diseases (leaf curl, leaf spot, white fly, yellowish leaf) were identified and examined using a pretrained deep learning networks (VGG16, VGG19, Inception V3, and Alex-net) using the dataset of chilli leafs with and without augmentation. The methodology involves a dataset of 2000 latest images of chilli leaves (apart from imagenet dataset) with four different illnesses to train and test the model, with various inputs and mini-batch sizes. The model’s performance of all the networks is assessed using a test dataset, and the results showed that Inception V3 achieved the highest accuracy rates of 93% in identifying these four disorders, followed by VGG16 (85%) and VGG19 (83%), respectively. The suggested methodology can be used by farmers as a decision support system to monitor the health of their crops and take timely and appropriate action that will ultimately help them sustain the output of chillies.
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Gulzar, Z., Chandu, S., Ravi, K. (2023). Classification and Analysis of Chilli Plant Disease Detection Using Convolution Neural Networks. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_45
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DOI: https://doi.org/10.1007/978-981-99-7093-3_45
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