Mayo Flavia, Maina Ciira, Mgala Mvurya, Mduma Neema
Computational and Communication Science Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania.
Electrical and Electronic Engineering, Dedan Kimathi University of Technology, Nyeri, Kenya.
Front Artif Intell. 2024 Aug 16;7:1384709. doi: 10.3389/frai.2024.1384709. eCollection 2024.
Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.
农业被视为坦桑尼亚经济的支柱，超过60%的居民依靠农业为生。玉米是该国的主要粮食作物，占所有农田产量的45%。然而，其生产力受到无法足够早地检测玉米疾病的限制。玉米条纹病毒（MSV）和玉米致死坏死病毒（MLN）是常见疾病，农民往往发现得太晚。这就导致需要开发一种早期检测这些疾病的方法，以便能够及时治疗。本研究调查了在坦桑尼亚开发用于早期检测玉米疾病的深度学习模型的潜力。收集数据的地区是阿鲁沙、乞力马扎罗和曼亚拉。数据是通过植物观察收集的。该研究提出了卷积神经网络（CNN）和视觉Transformer（ViT）模型。使用四类图像数据来训练这两种模型：MLN、健康、MSV和错误。结果显示，ViT模型超过了CNN模型，准确率分别为93.1%和90.96%。进一步的研究应侧重于移动应用程序开发以及更精确地部署该模型，以便在现实生活中早期检测上述疾病。
