Towards Leveraging AI Deep Learning Technology as a means to Smart Farming In Developing Countries: A case of Zambia

Abstract

Despite Plants being a major source of food for the world population, they continue being ravaged by plant diseases, a situation that greatly contributes to significant decline in production, which ultimately adversely impacts on food Security. Owing to the fact that manual plant disease monitoring is both laborious and error-prone, there has been a heightened need for farming practices that are sustainable, efficient, reliable and cost effective, necessitated by the need to adopt cutting edge technologies such as Artificial intelligence. Smart farming as an innovative approach to agriculture, offers farmers in developing countries a means to effectively diagnose and proactively manage plant diseases. Innovative smart farming solutions through the use of technology makes precision in agriculture possible, by enabling farmers to adopt practices that are optimized based on real-time data and analytics. The increased precision in dealing with problems such as crop disease detection help reduce consumables during disease maintenance, thereby increasing profitability and enhancing food security. This study aims to leverage technology as a means to smart farming in Zambia, a developing country in the sub-Saharan region of Africa, by employing a Convolutional Neural Network (CNN) model for the detection of tomato leaf diseases. Tomato production in Zambia faces significant challenges due to the prevalence of diseases such as early blight, late blight, and leaf mold, which can potentially lead to substantial crop losses. Nonetheless, Early and accurate detection of these diseases is crucial for effective management and increased productivity. This study proposes the use of Automated tomato leaf disease detection through the use of a convolutional neural network model. The plant village dataset which is one of the largest open access repository of expertly curated leaf images for disease diagnosis is used in this study. The CNN model is trained using this dataset, enabling it to learn discriminative features and patterns associated with different disease classes. This system offers an opportunity to empower farmers with timely and accurate information regarding disease occurrence and severity, enabling them to take proactive measures for disease management. By leveraging technology as a means to smart farming, the study aims to improve the efficiency, productivity, and sustainability of tomato farming in Zambia. The Convolutional neural network for the detection of tomato leaf disease was built, successfully trained and deployed. The accuracy of the CNN Model was at 95.8%