A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming

Abstract

Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run.

XAI‐driven model for crop recommender system for use in precision agriculture

“…To better comprehend the performance of the optimization algorithm with RBF, the model is evaluated with RBF with SMO and RBF alone. The performance of the models is compared with the outcomes of the studies presented by Raja et al 44 and Thilakarathne et al 45 The results are discussed in the current section. The corresponding confusion matrix obtained on experimentation with RBF alone is shown in Figure 11A and class‐wise results are shown in Table 1, and the confusion matrix with experimental results of RBF with SMO are shown in Figure 11B and their class‐wise results are shown in Table 2.…”

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

“…According to the optimum pH range that tomato plants can bear, which is between 5.5 to 6.8, it is evident that tomatoes can tolerate slightly acidic soils down to a pH of 5.5, but for the best harvest, it should be between 6.0 to 6.5 pH. On the other hand, tomatoes are an acid-loving plant that is best grown in soils with a pH below 7.0 ( Kagita et al., 2021 ; Quy et al., 2022 ; Thilakarathne et al., 2022 ), which we can clearly conclude soil pH conditions are optimum for our plants to grow well.…”

Digitalisation of agriculture in Zimbabwe: Challenges and opportunities

“…Due to deteriorating rate of soil moisture with negligible charge, there is a need to effectively and efficiently manage water so as to achieving a sustainable crop production (Benos et al, 2021). An improvement of water quality can be a result of effective water management, also including reduction of pollution and health risks (Thilakarathne, Bakar, Abas, & Yassin, 2022).…”

“…This allows for better decision making to maximise crop yield hence becoming more cost effective. ML through weather predictions, prevents farmers from wasting scarce resources like water and fertiliser through precision farming (Thilakarathne et al, 2022). Predicting the nature of rainfall, for example, can enlighten farmers on the type or variety of crop to farm.…”