Smart Farm-Care using a Deep Learning Model on Mobile Phones

Abstract

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

Irrigation Water Management in a Water-Scarce Environment in the Context of Climate Change

“…Smart farming, recharging wells, advanced irrigation systems, and crop pattern selection are examples of adaption solutions to the consequences of climate change. Smart farming was established in response to the issues that agricultural production faces in terms of productivity, food safety, and sustainability as a result of climate change (Francis et al, 2023). Pumping water into the aquifer might assist the growth of agricultural fields.…”