Maximizing a Farm Yield Through Precision Agriculture utilizing Fourth Industrial Revolution (4IR) Tools and Space Technology

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 its 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 of 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 up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

“…Accurate and high-throughput plant phenotyping is important for accelerating crop breeding [52]. This technique uses advanced technologies such as remote sensing [40,42], spectral imaging [41], and robotics [53] to collect large amounts of data on plant characteristics, such as growth rate, yield, disease resistance, and morphology [51,54,55]. By collecting and analyzing these data, farmers can gain insights into how their crops are performing and make more informed decisions about things like fertilization, irrigation, harvesting, and pest management [22,54].…”

“…This technique uses advanced technologies such as remote sensing [40,42], spectral imaging [41], and robotics [53] to collect large amounts of data on plant characteristics, such as growth rate, yield, disease resistance, and morphology [51,54,55]. By collecting and analyzing these data, farmers can gain insights into how their crops are performing and make more informed decisions about things like fertilization, irrigation, harvesting, and pest management [22,54]. High-throughput phenotyping can also help breeders to develop new crop varieties that are better adapted to local growing conditions and can produce higher yields (Figure 4) [51].…”

“…A full range of visible and near-infrared hyperspectral data enables ML techniques such as LSR (least squares regression) to predict specific biochemical and physicochemical traits beyond simple vegetative indices [56]. ML-based precision agriculture systems have AI background [52,54], and therefore, when detecting diseases, pests, nutrient deficiency, and weeds, stressed responses are detected using high-quality images generated with UGV or UAV remote sensing, hyperspectral imaging, and satellite imaging to support high-throughput phenotyping [57]. Ultimately, high-throughput phenotyping has the potential to revolutionize agriculture by enabling more the precise, real-time, and efficient monitoring of farming practices that can improve crop productivity, reduce environmental impacts, and increase food security [54].…”

“…AI background [52,54], and therefore, when detecting diseases, pests, nutrient deficiency, and weeds, stressed responses are detected using high-quality images generated with UGV or UAV remote sensing, hyperspectral imaging, and satellite imaging to support high-throughput phenotyping [57]. Ultimately, high-throughput phenotyping has the potential to revolutionize agriculture by enabling more the precise, real-time, and efficient monitoring of farming practices that can improve crop productivity, reduce environmental impacts, and increase food security [54]. The traditional methods of plant breeding have limitations in terms of time, cost, and accuracy.…”