A Review of Plant Disease Detection Systems for Farming Applications

Abstract

The globe and more particularly the economically developed regions of the world are currently in the era of the Fourth Industrial Revolution (4IR). Conversely, the economically developing regions in the world (and more particularly the African continent) have not yet even fully passed through the Third Industrial Revolution (3IR) wave, and Africa’s economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth in the global human population, which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet the steep food demand and stabilize the economies of the African continent and countries such as India that are dependent on the agricultural sector to some extent. Technological advances in precision agriculture are already improving farm yields, although several opportunities for further improvement still exist. This study evaluated plant disease detection models (in particular, those over the past two decades) while aiming to gauge the status of the research in this area and identify the opportunities for further research. This study realized that little literature has discussed the real-time monitoring of the onset signs of diseases before they spread throughout the whole plant. There was also substantially less focus on real-time mitigation measures such as actuation operations, spraying pesticides, spraying fertilizers, etc., once a disease was identified. Very little research has focused on the combination of monitoring and phenotyping functions into one model capable of multiple tasks. Hence, this study highlighted a few opportunities for further focus.

Plant phenotyping relevance

植物病害検出モデルをレビューし、植物の症状・病害状態を観測する検出手法を扱う方法論レビューであるため、病害フェノタイピング手法の方法レビューとして中心的です。

Code and data availability

This is a review article of plant disease detection systems. The authors explicitly state no data was created ('This research study is a review paper and hence no data was created'), and any data would only be available upon request. The URLs in Table 2 are generic third-party ML libraries (TensorFlow, Caffe, etc.), i.

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