Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.

Capacity development interventions are considered critical entry points for advancing gender equality in agricultural research systems. However, the impacts of capacity development programs are often difficult to track. Academic reviews highlight that insufficient attention is paid to the suitability of gender training programs to increase capacity and limited evidence is available on their longer-term impacts.

Though extension services have long since proved their value to agricultural production and farmer prosperity, their record in sub-Saharan Africa has been mixed. To study the impact of such programs on farmers' learning about agricultural technologies, we implemented a quasi-randomized controlled trial and collected detailed panel data among Malawian farmers. Based on those findings, we develop a two-stage learning framework, in which farmers formulate yield expectations before deciding on how much effort to invest in learning about these processes.

This article presents lessons from the rich adoption literature for the nascent research on adaptation. Individuals' adoption choices are affected by profit and risk considerations and by credit and biophysical constraints. New technologies spread gradually, reflecting heterogeneity among potential adopters, processes of learning and technological improvement, and policies and institutions. Adaptation is the response of economic agents and societies to major shocks. We distinguish between reactive and proactive adaptation.

This paper analyses a monitoring, evaluation and learning (MEL) system developed within an agricultural research for development institution. The system applies aspects of the Outcome Harvesting tool and focuses on learning for adaptation and improvement of innovation processes. Developmental evaluation principles are applied to discuss its application. The MEL system provides insight into the processes and interactions with next users that generate outcomes.

This toolbox has been developed to collate different tools and methods that can be used for food system analysis.
It is specifically based on systems thinking for food system analysis, with the aim to formulate actionable recommendations that can bring about systemic change.It describes both the process of a food system analysis, as well as a set of tools that can be used at different stages.