# A Review of Plant Disease Detection Systems for Farming Applications
## 1 Introduction
Although African leaders and officials have spoken out in support of 4 IR's goals, most of the continent's nations continue to be heavily dependent on an agarian economy [10]. [PERSON] [5] stated that critics frequently advocate that some community ICT projects have been unsuccessful partly because of the technology/reality divide. Africa has previously been described as a technological and digital wilderness [3; 10; 11]. It is evident that Africa still lags the rest of the international community regarding the Fourth Industrial Revolution. This is due to several factors such as poor infrastructure and over-reliance on the primary sector--agriculture [6]. Agriculture remains the backbone of the African continent; it is a crucial part of the global economy and plays an important role in providing food for the rapidly growing population and hence its heightened food demand [8; 10]. According to the United Nations, the world's population is anticipated to reach over 10 billion people by 2050, virtually doubling global food consumption [3]. Therefore, global agricultural productivity will need to rise by 1.75% each year to meet the resulting food demand [3; 11]. The Global Harvest Initiative (GHI) estimated that productivity is currently increasing at a rate of 1.63% annually since the farmers are already being assisted by precision agriculture and advanced technologies such as automation, machine learning, computer vision, and artificial intelligence in keeping up with the food demand [5]. Global navigation satellite systems (GNSSs) are playing a particularly significant role as enablers in the transformation of the agricultural sector through precision agriculture. [PERSON] [12] defined precision agriculture as a smart form of farm governance using digital systems, sensors, microcontrollers, actuators, robotics, and communication systems to achieve the goals of sustainability, revenue, and environmental conservation. [PERSON] [8] defined it as the integration of different computer tools into conventional agricultural methods to maximize the farm harvest and achieve self-sufficiency in farming operations. Precision agriculture (also known as digital farming or intelligent agriculture) includes (but is not limited to) the following: pest detection, weed detection, plant disease detection, morphology, irrigation monitoring and control, soil monitoring, air monitoring, humidity monitoring, and harvesting [4; 6; 7; 8; 12]. This paper aimed to study in detail the recent research trends in precision agriculture--particularly in the disease/pest/weed detection area--to comprehend the artificial intelligence (AI) tools and scientific background required to implement these machine learning (ML)-based precision agriculture systems. The disease/pest/weed detection system was chosen because it possesses a multipurpose architecture that can be applied in several diverse applications on a farm with only amendments to the software and limited changes to the hardware; for example, a disease, weed, pest, nutrient deficiency, or morphological feature. Detection systems all have similar working principles in which a high-quality picture is acquired from a farm specimen, and an ML algorithm is then fed that picture after processing to classify what it detected in the given picture. Therefore, these systems can have similar prototypic architectures, and a farmer can have one universal robotic system that has a few changeable parts (such as cameras and sensors) and different software that are specific to different activities. This paper aimed to present and summarize the recent research trends in precision agriculture--particularly in the disease/pest/weed detection area--to identify the opportunities for further research. Its general architecture can be seen in Figure 2. The following research questions were addressed in this study:  
* What are the recent precision agriculture research developments, particularly for disease/pest/weed detection systems? * What are the found limitations and gaps in the literature review?