An Outlook on Harnessing Technological Innovative Competence in Sustainably Transforming African Agriculture

Agricultural value chains worldwide provide essential support to livelihoods, ecosystem services, and the growing bioeconomy. The coronavirus disease 2019 (COVID-19) pandemic slowed down or reversed decades of agricultural growth and exposed the vulnerabilities of farmers and the food insecure in Africa, thus reiterating the need to build resilience, agility, and adaptability for a sustainable agriculture. Existing social, political, environmental, and economic challenges demonstrate that a path to faster sustainable growth is increased productivity through improved input quality, of which technical inputs are a part. This work presents a perspective calling for African innovative competence in technological and methodological applications and solutions as part of the most critical area of a holistic organization for social progress. It finds that while performances of previous agricultural transformation efforts offer insights for future directions, novel pathways fitting to the diversity of situations and contexts on the continent are needed. These may include vertical agriculture in land-constrained regions to grow high-value products, ocean or sea farming in coastal regions, development of multiple-harvesting crops, and self-replicating plants. Developing standards that integrate current scientific methodologies and technologies with indigenous knowledge for agricultural growth and disaster management will bring the complementary benefits of both worlds into optimal development.

A genetic programming-based optimal sensor placement for greenhouse monitoring and control

Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson's correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility.

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system's internal environment with the highest occurring in May. In May, an average change of -0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

Technological Advancements and Economics in Plant Production Systems: How to Retrofit?

Plant production systems such as plant factories and greenhouses can help promote resilience in food production. These systems could be used for plant protection and aid in controlling the micro- and macro- environments needed for optimal plant growth irrespective of natural disasters and changing climate conditions. However, to ensure optimal environmental controls and efficient production, several technologies such as sensors and robots have been developed and are at different stages of implementation. New and improved systems are continuously being investigated and developed with technological advances such as robotics, sensing, and artificial intelligence to mitigate hazards to humans working in these systems from poor ventilation and harsh weather while improving productivity. These technological advances necessitate frequent retrofits considering local contexts such as present and projected labor costs. The type of agricultural products also affects measures to be implemented to maximize returns on investment. Consequently, we formulated the retrofitting problem for plant production systems considering two objectives; minimizing the total cost for retrofitting and maximizing the yearly net profit. Additionally, we considered the following: (a) cost of new technologies; (b) present and projected cost for human labor and robotics; (c) size and service life of the plant production system; (d) productivity before and after retrofit, (e) interest on loans for retrofitting, (f) energy consumption before and after retrofit and, (g) replacement and maintenance cost of systems. We solved this problem using a multi-objective evolutionary algorithm that results in a set of compromised solutions and performed several simulations to demonstrate the applicability and robustness of the method. Results showed up to a 250% increase in annual net profits in an investigated case, indicating that the availability of all the possible retrofitting combinations would improve decision making. A user-friendly system was developed to provide all the feasible retrofitting combinations and total costs with the yearly return on investment in agricultural production systems in a single run.